import warnings
warnings.filterwarnings("ignore")
import pandas as pd
import scipy.stats as stats
import numpy as np
from pandas.api.types import is_numeric_dtype
import statsmodels.api as sm
import seaborn as sns
from seaborn_qqplot import QQPlot
from matplotlib import pyplot as plt
from scipy.stats import gamma
from sklearn.preprocessing import StandardScaler
import pickle
from sklearn.metrics import mean_squared_error
#-------Importing tensorflow libraries-------#
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense,Dropout
from tensorflow.python.keras.wrappers.scikit_learn import KerasRegressor
#------split and pipeline libraries------#
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import KFold
from sklearn.pipeline import Pipeline
#--------------------------------------------------#
from statsmodels.stats.outliers_influence import variance_inflation_factor
sns.set(color_codes=True)
%matplotlib inline
df = pd.read_csv("Part- 1,2&3 - Signal.csv")
df.head()
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | Signal_Strength | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
| 1 | 7.8 | 0.88 | 0.00 | 2.6 | 0.098 | 25.0 | 67.0 | 0.9968 | 3.20 | 0.68 | 9.8 | 5 |
| 2 | 7.8 | 0.76 | 0.04 | 2.3 | 0.092 | 15.0 | 54.0 | 0.9970 | 3.26 | 0.65 | 9.8 | 5 |
| 3 | 11.2 | 0.28 | 0.56 | 1.9 | 0.075 | 17.0 | 60.0 | 0.9980 | 3.16 | 0.58 | 9.8 | 6 |
| 4 | 7.4 | 0.70 | 0.00 | 1.9 | 0.076 | 11.0 | 34.0 | 0.9978 | 3.51 | 0.56 | 9.4 | 5 |
df.isnull().any()
Parameter 1 False Parameter 2 False Parameter 3 False Parameter 4 False Parameter 5 False Parameter 6 False Parameter 7 False Parameter 8 False Parameter 9 False Parameter 10 False Parameter 11 False Signal_Strength False dtype: bool
df.info(verbose=True)
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1599 entries, 0 to 1598 Data columns (total 12 columns): Parameter 1 1599 non-null float64 Parameter 2 1599 non-null float64 Parameter 3 1599 non-null float64 Parameter 4 1599 non-null float64 Parameter 5 1599 non-null float64 Parameter 6 1599 non-null float64 Parameter 7 1599 non-null float64 Parameter 8 1599 non-null float64 Parameter 9 1599 non-null float64 Parameter 10 1599 non-null float64 Parameter 11 1599 non-null float64 Signal_Strength 1599 non-null int64 dtypes: float64(11), int64(1) memory usage: 150.0 KB
df.describe()
| Parameter 1 | Parameter 2 | Parameter 3 | Parameter 4 | Parameter 5 | Parameter 6 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | Signal_Strength | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 | 1599.000000 |
| mean | 8.319637 | 0.527821 | 0.270976 | 2.538806 | 0.087467 | 15.874922 | 46.467792 | 0.996747 | 3.311113 | 0.658149 | 10.422983 | 5.636023 |
| std | 1.741096 | 0.179060 | 0.194801 | 1.409928 | 0.047065 | 10.460157 | 32.895324 | 0.001887 | 0.154386 | 0.169507 | 1.065668 | 0.807569 |
| min | 4.600000 | 0.120000 | 0.000000 | 0.900000 | 0.012000 | 1.000000 | 6.000000 | 0.990070 | 2.740000 | 0.330000 | 8.400000 | 3.000000 |
| 25% | 7.100000 | 0.390000 | 0.090000 | 1.900000 | 0.070000 | 7.000000 | 22.000000 | 0.995600 | 3.210000 | 0.550000 | 9.500000 | 5.000000 |
| 50% | 7.900000 | 0.520000 | 0.260000 | 2.200000 | 0.079000 | 14.000000 | 38.000000 | 0.996750 | 3.310000 | 0.620000 | 10.200000 | 6.000000 |
| 75% | 9.200000 | 0.640000 | 0.420000 | 2.600000 | 0.090000 | 21.000000 | 62.000000 | 0.997835 | 3.400000 | 0.730000 | 11.100000 | 6.000000 |
| max | 15.900000 | 1.580000 | 1.000000 | 15.500000 | 0.611000 | 72.000000 | 289.000000 | 1.003690 | 4.010000 | 2.000000 | 14.900000 | 8.000000 |
columns = list(df.columns)
for column in columns:
plt.figure(figsize=(10, 5))
sns.distplot(df[column], color = "blue").set_title("Distribution of "+column)
for column in columns:
plt.figure(figsize=(10, 5))
sns.boxplot(x='Signal_Strength',y=column, data = df, hue = 'Signal_Strength')
for column in df.columns:
print(100*"*")
print("Mean of "+str(column)+"="+str(df[column].mean()))
print("Median of "+str(column)+"="+str(df[column].median()))
print("Mode of "+str(column)+"="+str(df[column].mode()[0]))
print("Skewness in "+str(column)+"="+str(df[column].skew()))
print("Excess Kurtosis in "+str(column)+"="+str(df[column].kurtosis()))
print(100*"*")
**************************************************************************************************** Mean of Parameter 1=8.319637273295838 Median of Parameter 1=7.9 Mode of Parameter 1=7.2 Skewness in Parameter 1=0.9827514413284587 Excess Kurtosis in Parameter 1=1.1321433977276252 **************************************************************************************************** **************************************************************************************************** Mean of Parameter 2=0.5278205128205131 Median of Parameter 2=0.52 Mode of Parameter 2=0.6 Skewness in Parameter 2=0.6715925723840199 Excess Kurtosis in Parameter 2=1.2255422501791422 **************************************************************************************************** **************************************************************************************************** Mean of Parameter 3=0.2709756097560964 Median of Parameter 3=0.26 Mode of Parameter 3=0.0 Skewness in Parameter 3=0.3183372952546368 Excess Kurtosis in Parameter 3=-0.7889975153633966 **************************************************************************************************** **************************************************************************************************** Mean of Parameter 4=2.5388055034396517 Median of Parameter 4=2.2 Mode of Parameter 4=2.0 Skewness in Parameter 4=4.54065542590319 Excess Kurtosis in Parameter 4=28.617595424475443 **************************************************************************************************** **************************************************************************************************** Mean of Parameter 5=0.08746654158849257 Median of Parameter 5=0.079 Mode of Parameter 5=0.08 Skewness in Parameter 5=5.680346571971722 Excess Kurtosis in Parameter 5=41.71578724757661 **************************************************************************************************** **************************************************************************************************** Mean of Parameter 6=15.874921826141339 Median of Parameter 6=14.0 Mode of Parameter 6=6.0 Skewness in Parameter 6=1.250567293314441 Excess Kurtosis in Parameter 6=2.023562045840575 **************************************************************************************************** **************************************************************************************************** Mean of Parameter 7=46.46779237023139 Median of Parameter 7=38.0 Mode of Parameter 7=28.0 Skewness in Parameter 7=1.515531257594554 Excess Kurtosis in Parameter 7=3.8098244878645744 **************************************************************************************************** **************************************************************************************************** Mean of Parameter 8=0.9967466791744833 Median of Parameter 8=0.99675 Mode of Parameter 8=0.9972 Skewness in Parameter 8=0.07128766294945525 Excess Kurtosis in Parameter 8=0.9340790654648083 **************************************************************************************************** **************************************************************************************************** Mean of Parameter 9=3.311113195747343 Median of Parameter 9=3.31 Mode of Parameter 9=3.3 Skewness in Parameter 9=0.19368349811284427 Excess Kurtosis in Parameter 9=0.806942508246574 **************************************************************************************************** **************************************************************************************************** Mean of Parameter 10=0.6581488430268921 Median of Parameter 10=0.62 Mode of Parameter 10=0.6 Skewness in Parameter 10=2.4286723536602945 Excess Kurtosis in Parameter 10=11.720250727147674 **************************************************************************************************** **************************************************************************************************** Mean of Parameter 11=10.422983114446502 Median of Parameter 11=10.2 Mode of Parameter 11=9.5 Skewness in Parameter 11=0.8608288069184189 Excess Kurtosis in Parameter 11=0.20002931143836733 **************************************************************************************************** **************************************************************************************************** Mean of Signal_Strength=5.6360225140712945 Median of Signal_Strength=6.0 Mode of Signal_Strength=5 Skewness in Signal_Strength=0.21780157547366327 Excess Kurtosis in Signal_Strength=0.2967081197538759 ****************************************************************************************************
Observations:
sns.pairplot(df)
<seaborn.axisgrid.PairGrid at 0x10b30248>
Observation:We can see some correlation between parameter 1 and parameter 8 but we need to check the correlation statistically
df1 = df.copy()
for column in df1.drop(columns=['Signal_Strength']).columns:
df1[column] = df1[column].apply(lambda x: x if
((x - df1[column].mean())/df1[column].std()) <= 3
else
df1[column].median()
#np.nan
)
def backwardElimination(x, Y, significance_level, columns):
numVars = len(x[0])
for i in range(0, numVars):
regressor_OLS = sm.OLS(Y, x).fit()
maxVar = max(regressor_OLS.pvalues)
if maxVar > significance_level:
for j in range(0, numVars - i):
if (regressor_OLS.pvalues[j] == maxVar):
x = np.delete(x, j, 1)
columns = np.delete(columns, j)
regressor_OLS.summary()
return x, columns
x_ols = df1.drop(columns=['Signal_Strength']).values
y_ols = df1['Signal_Strength']
significance_level = 0.05
selected_columns = df1.drop(columns=['Signal_Strength']).columns
data, selected_columns = backwardElimination(x_ols, y_ols, significance_level, selected_columns)
selected_columns
Index(['Parameter 2', 'Parameter 5', 'Parameter 7', 'Parameter 8',
'Parameter 9', 'Parameter 10', 'Parameter 11'],
dtype='object')
selected_columns = list(selected_columns)
selected_columns.append('Signal_Strength')
After_backward_elimination = df[selected_columns].copy()
plt.figure(figsize=(20, 20))
df_corr = After_backward_elimination.drop(columns=['Signal_Strength']).corr(method='pearson')
ax = sns.heatmap(df_corr, annot=True, cmap='YlGnBu')
bottom, top = ax.get_ylim()
ax.set_ylim(bottom + 0.5, top - 0.5)
(7.0, 0.0)
Observations:
X = df.drop(columns=['Signal_Strength'])
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values,i) for i in range(len(X.columns))]
vif_data
| feature | VIF | |
|---|---|---|
| 0 | Parameter 1 | 74.452265 |
| 1 | Parameter 2 | 17.060026 |
| 2 | Parameter 3 | 9.183495 |
| 3 | Parameter 4 | 4.662992 |
| 4 | Parameter 5 | 6.554877 |
| 5 | Parameter 6 | 6.442682 |
| 6 | Parameter 7 | 6.519699 |
| 7 | Parameter 8 | 1479.287209 |
| 8 | Parameter 9 | 1070.967685 |
| 9 | Parameter 10 | 21.590621 |
| 10 | Parameter 11 | 124.394866 |
sns.pairplot(df1)
<seaborn.axisgrid.PairGrid at 0x87796a08>
X = df1.drop(columns=['Signal_Strength'])
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values,i) for i in range(len(X.columns))]
vif_data
| feature | VIF | |
|---|---|---|
| 0 | Parameter 1 | 67.525613 |
| 1 | Parameter 2 | 17.700405 |
| 2 | Parameter 3 | 8.259058 |
| 3 | Parameter 4 | 9.553091 |
| 4 | Parameter 5 | 16.211930 |
| 5 | Parameter 6 | 6.542079 |
| 6 | Parameter 7 | 6.370576 |
| 7 | Parameter 8 | 1331.472877 |
| 8 | Parameter 9 | 960.386214 |
| 9 | Parameter 10 | 29.009573 |
| 10 | Parameter 11 | 133.234695 |
Observations:
scaled = df1.copy()
for column in scaled.drop(columns=['Signal_Strength']).columns:
scaled[column] = scaled[column].apply(lambda x:
(x - scaled[column].mean())/scaled[column].std()
)
from sklearn.decomposition import PCA
features = list(scaled.drop(columns=['Signal_Strength']).columns)
pca = PCA(n_components=10)
pca.fit(np.array(scaled[features]))
variance = pca.explained_variance_ratio_
var = np.cumsum(np.round(variance,decimals = 3)*100)
var
array([27.4, 45.1, 59.4, 69.7, 77.9, 84.7, 89.9, 94. , 97. , 99.1])
plt.ylabel("% Variance explained")
plt.xlabel("Number of features")
plt.title("PCA analysis")
plt.ylim(20,110)
plt.xlim(0,11)
plt.plot(var)
[<matplotlib.lines.Line2D at 0x8928ebc8>]
Observations: We could pick n_components = 6 as they account for 90% of variance in the dataset
pca = PCA(n_components=6)
principalComponents = pca.fit_transform(np.array(scaled[features]))
pcDf = pd.DataFrame(data = principalComponents
, columns = ['PC1', 'PC2','PC3','PC4','PC5','PC6'])
finalDf = pd.concat([pcDf,scaled[['Signal_Strength']]], axis=1)
X = finalDf.drop(columns=['Signal_Strength'])
vif_data = pd.DataFrame()
vif_data['feature'] = X.columns
vif_data['VIF'] = [variance_inflation_factor(X.values,i) for i in range(len(X.columns))]
vif_data
| feature | VIF | |
|---|---|---|
| 0 | PC1 | 1.0 |
| 1 | PC2 | 1.0 |
| 2 | PC3 | 1.0 |
| 3 | PC4 | 1.0 |
| 4 | PC5 | 1.0 |
| 5 | PC6 | 1.0 |
sns.pairplot(finalDf)
<seaborn.axisgrid.PairGrid at 0x8a523c48>
xpca = finalDf.drop(columns=['Signal_Strength']).values
ypca = finalDf['Signal_Strength'].values
X_train, X_test, y_train, y_test = train_test_split(xpca, ypca,test_size=0.33, random_state=42)
model = Sequential()
model.add(Dense(10, input_dim=6, kernel_initializer='normal', activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(10, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(1, activation='linear'))
model.summary()
Model: "sequential_18" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_55 (Dense) (None, 10) 70 _________________________________________________________________ dropout_30 (Dropout) (None, 10) 0 _________________________________________________________________ dense_56 (Dense) (None, 10) 110 _________________________________________________________________ dropout_31 (Dropout) (None, 10) 0 _________________________________________________________________ dense_57 (Dense) (None, 1) 11 ================================================================= Total params: 191 Trainable params: 191 Non-trainable params: 0 _________________________________________________________________
model.compile(loss='mse', optimizer='adam', metrics=['mse','mae'])
history = model.fit(X_train, y_train, epochs=200, batch_size=10, verbose=1, validation_split=0.2)
Train on 856 samples, validate on 215 samples Epoch 1/200 856/856 [==============================] - 1s 707us/sample - loss: 26.6416 - mean_squared_error: 26.6416 - mean_absolute_error: 5.0784 - val_loss: 20.7450 - val_mean_squared_error: 20.7450 - val_mean_absolute_error: 4.4613 Epoch 2/200 856/856 [==============================] - 0s 152us/sample - loss: 14.6958 - mean_squared_error: 14.6958 - mean_absolute_error: 3.5782 - val_loss: 7.6434 - val_mean_squared_error: 7.6434 - val_mean_absolute_error: 2.4951 Epoch 3/200 856/856 [==============================] - 0s 153us/sample - loss: 5.9726 - mean_squared_error: 5.9726 - mean_absolute_error: 2.0134 - val_loss: 2.1598 - val_mean_squared_error: 2.1598 - val_mean_absolute_error: 1.2352 Epoch 4/200 856/856 [==============================] - 0s 155us/sample - loss: 3.9955 - mean_squared_error: 3.9955 - mean_absolute_error: 1.6084 - val_loss: 1.4927 - val_mean_squared_error: 1.4927 - val_mean_absolute_error: 1.0268 Epoch 5/200 856/856 [==============================] - 0s 154us/sample - loss: 3.5079 - mean_squared_error: 3.5079 - mean_absolute_error: 1.5178 - val_loss: 1.0485 - val_mean_squared_error: 1.0485 - val_mean_absolute_error: 0.8394 Epoch 6/200 856/856 [==============================] - 0s 157us/sample - loss: 3.3699 - mean_squared_error: 3.3699 - mean_absolute_error: 1.4761 - val_loss: 0.9247 - val_mean_squared_error: 0.9247 - val_mean_absolute_error: 0.7897 Epoch 7/200 856/856 [==============================] - 0s 151us/sample - loss: 3.1335 - mean_squared_error: 3.1335 - mean_absolute_error: 1.4103 - val_loss: 0.8207 - val_mean_squared_error: 0.8207 - val_mean_absolute_error: 0.7274 Epoch 8/200 856/856 [==============================] - 0s 152us/sample - loss: 3.1021 - mean_squared_error: 3.1021 - mean_absolute_error: 1.4162 - val_loss: 0.5782 - val_mean_squared_error: 0.5782 - val_mean_absolute_error: 0.5825 Epoch 9/200 856/856 [==============================] - 0s 152us/sample - loss: 2.7783 - mean_squared_error: 2.7783 - mean_absolute_error: 1.3228 - val_loss: 0.6557 - val_mean_squared_error: 0.6557 - val_mean_absolute_error: 0.6462 Epoch 10/200 856/856 [==============================] - 0s 153us/sample - loss: 2.8290 - mean_squared_error: 2.8290 - mean_absolute_error: 1.3497 - val_loss: 0.6596 - val_mean_squared_error: 0.6596 - val_mean_absolute_error: 0.6508 Epoch 11/200 856/856 [==============================] - 0s 178us/sample - loss: 2.4912 - mean_squared_error: 2.4912 - mean_absolute_error: 1.2442 - val_loss: 0.5894 - val_mean_squared_error: 0.5894 - val_mean_absolute_error: 0.6055 Epoch 12/200 856/856 [==============================] - 0s 159us/sample - loss: 2.3763 - mean_squared_error: 2.3763 - mean_absolute_error: 1.2252 - val_loss: 0.5144 - val_mean_squared_error: 0.5144 - val_mean_absolute_error: 0.5552 Epoch 13/200 856/856 [==============================] - 0s 158us/sample - loss: 2.4728 - mean_squared_error: 2.4728 - mean_absolute_error: 1.2288 - val_loss: 0.5551 - val_mean_squared_error: 0.5551 - val_mean_absolute_error: 0.5704 Epoch 14/200 856/856 [==============================] - 0s 153us/sample - loss: 2.2382 - mean_squared_error: 2.2382 - mean_absolute_error: 1.1931 - val_loss: 0.4569 - val_mean_squared_error: 0.4569 - val_mean_absolute_error: 0.5213 Epoch 15/200 856/856 [==============================] - 0s 155us/sample - loss: 2.2683 - mean_squared_error: 2.2683 - mean_absolute_error: 1.2047 - val_loss: 0.5003 - val_mean_squared_error: 0.5003 - val_mean_absolute_error: 0.5330 Epoch 16/200 856/856 [==============================] - 0s 153us/sample - loss: 2.1854 - mean_squared_error: 2.1854 - mean_absolute_error: 1.1807 - val_loss: 0.4439 - val_mean_squared_error: 0.4439 - val_mean_absolute_error: 0.5068 Epoch 17/200 856/856 [==============================] - 0s 162us/sample - loss: 2.2764 - mean_squared_error: 2.2764 - mean_absolute_error: 1.2038 - val_loss: 0.4102 - val_mean_squared_error: 0.4102 - val_mean_absolute_error: 0.4913 Epoch 18/200 856/856 [==============================] - 0s 150us/sample - loss: 2.2475 - mean_squared_error: 2.2475 - mean_absolute_error: 1.1967 - val_loss: 0.5532 - val_mean_squared_error: 0.5532 - val_mean_absolute_error: 0.5786 Epoch 19/200 856/856 [==============================] - 0s 150us/sample - loss: 2.1785 - mean_squared_error: 2.1785 - mean_absolute_error: 1.1729 - val_loss: 0.4127 - val_mean_squared_error: 0.4127 - val_mean_absolute_error: 0.4896 Epoch 20/200 856/856 [==============================] - 0s 159us/sample - loss: 2.1451 - mean_squared_error: 2.1451 - mean_absolute_error: 1.1709 - val_loss: 0.4805 - val_mean_squared_error: 0.4805 - val_mean_absolute_error: 0.5318 Epoch 21/200 856/856 [==============================] - 0s 167us/sample - loss: 2.1476 - mean_squared_error: 2.1476 - mean_absolute_error: 1.1759 - val_loss: 0.5513 - val_mean_squared_error: 0.5513 - val_mean_absolute_error: 0.5705 Epoch 22/200 856/856 [==============================] - 0s 152us/sample - loss: 1.8763 - mean_squared_error: 1.8763 - mean_absolute_error: 1.0844 - val_loss: 0.5015 - val_mean_squared_error: 0.5015 - val_mean_absolute_error: 0.5538 Epoch 23/200 856/856 [==============================] - 0s 162us/sample - loss: 2.0451 - mean_squared_error: 2.0451 - mean_absolute_error: 1.1283 - val_loss: 0.4595 - val_mean_squared_error: 0.4595 - val_mean_absolute_error: 0.5269 Epoch 24/200 856/856 [==============================] - 0s 159us/sample - loss: 1.8106 - mean_squared_error: 1.8106 - mean_absolute_error: 1.0761 - val_loss: 0.4381 - val_mean_squared_error: 0.4381 - val_mean_absolute_error: 0.5112 Epoch 25/200 856/856 [==============================] - 0s 155us/sample - loss: 1.8514 - mean_squared_error: 1.8514 - mean_absolute_error: 1.0891 - val_loss: 0.4892 - val_mean_squared_error: 0.4892 - val_mean_absolute_error: 0.5356 Epoch 26/200 856/856 [==============================] - 0s 152us/sample - loss: 2.0447 - mean_squared_error: 2.0447 - mean_absolute_error: 1.1280 - val_loss: 0.5103 - val_mean_squared_error: 0.5103 - val_mean_absolute_error: 0.5531 Epoch 27/200 856/856 [==============================] - 0s 155us/sample - loss: 1.8961 - mean_squared_error: 1.8961 - mean_absolute_error: 1.0948 - val_loss: 0.4215 - val_mean_squared_error: 0.4215 - val_mean_absolute_error: 0.4882 Epoch 28/200 856/856 [==============================] - 0s 169us/sample - loss: 1.7215 - mean_squared_error: 1.7215 - mean_absolute_error: 1.0489 - val_loss: 0.4437 - val_mean_squared_error: 0.4437 - val_mean_absolute_error: 0.5110 Epoch 29/200 856/856 [==============================] - 0s 154us/sample - loss: 1.6555 - mean_squared_error: 1.6555 - mean_absolute_error: 1.0281 - val_loss: 0.4133 - val_mean_squared_error: 0.4133 - val_mean_absolute_error: 0.4929 Epoch 30/200 856/856 [==============================] - 0s 155us/sample - loss: 1.8992 - mean_squared_error: 1.8992 - mean_absolute_error: 1.0797 - val_loss: 0.5089 - val_mean_squared_error: 0.5089 - val_mean_absolute_error: 0.5518 Epoch 31/200 856/856 [==============================] - 0s 155us/sample - loss: 1.5771 - mean_squared_error: 1.5771 - mean_absolute_error: 1.0058 - val_loss: 0.4004 - val_mean_squared_error: 0.4004 - val_mean_absolute_error: 0.4741 Epoch 32/200 856/856 [==============================] - 0s 164us/sample - loss: 1.6944 - mean_squared_error: 1.6944 - mean_absolute_error: 1.0220 - val_loss: 0.4212 - val_mean_squared_error: 0.4212 - val_mean_absolute_error: 0.4917 Epoch 33/200 856/856 [==============================] - 0s 148us/sample - loss: 1.5534 - mean_squared_error: 1.5534 - mean_absolute_error: 0.9894 - val_loss: 0.4081 - val_mean_squared_error: 0.4081 - val_mean_absolute_error: 0.4857 Epoch 34/200 856/856 [==============================] - 0s 155us/sample - loss: 1.5091 - mean_squared_error: 1.5091 - mean_absolute_error: 0.9746 - val_loss: 0.4042 - val_mean_squared_error: 0.4042 - val_mean_absolute_error: 0.4812 Epoch 35/200 856/856 [==============================] - 0s 155us/sample - loss: 1.5923 - mean_squared_error: 1.5923 - mean_absolute_error: 0.9987 - val_loss: 0.4152 - val_mean_squared_error: 0.4152 - val_mean_absolute_error: 0.4842 Epoch 36/200 856/856 [==============================] - 0s 162us/sample - loss: 1.6037 - mean_squared_error: 1.6037 - mean_absolute_error: 0.9964 - val_loss: 0.3838 - val_mean_squared_error: 0.3838 - val_mean_absolute_error: 0.4648 Epoch 37/200 856/856 [==============================] - 0s 150us/sample - loss: 1.4778 - mean_squared_error: 1.4778 - mean_absolute_error: 0.9428 - val_loss: 0.3943 - val_mean_squared_error: 0.3943 - val_mean_absolute_error: 0.4747 Epoch 38/200 856/856 [==============================] - 0s 138us/sample - loss: 1.4798 - mean_squared_error: 1.4798 - mean_absolute_error: 0.9551 - val_loss: 0.4481 - val_mean_squared_error: 0.4481 - val_mean_absolute_error: 0.5135 Epoch 39/200 856/856 [==============================] - 0s 148us/sample - loss: 1.4415 - mean_squared_error: 1.4415 - mean_absolute_error: 0.9435 - val_loss: 0.4416 - val_mean_squared_error: 0.4416 - val_mean_absolute_error: 0.4973 Epoch 40/200 856/856 [==============================] - 0s 139us/sample - loss: 1.4665 - mean_squared_error: 1.4665 - mean_absolute_error: 0.9580 - val_loss: 0.4087 - val_mean_squared_error: 0.4087 - val_mean_absolute_error: 0.4825 Epoch 41/200 856/856 [==============================] - 0s 137us/sample - loss: 1.4795 - mean_squared_error: 1.4795 - mean_absolute_error: 0.9752 - val_loss: 0.4107 - val_mean_squared_error: 0.4107 - val_mean_absolute_error: 0.4874 Epoch 42/200 856/856 [==============================] - 0s 146us/sample - loss: 1.4016 - mean_squared_error: 1.4016 - mean_absolute_error: 0.9315 - val_loss: 0.3947 - val_mean_squared_error: 0.3947 - val_mean_absolute_error: 0.4763 Epoch 43/200 856/856 [==============================] - 0s 154us/sample - loss: 1.3179 - mean_squared_error: 1.3179 - mean_absolute_error: 0.9140 - val_loss: 0.4498 - val_mean_squared_error: 0.4498 - val_mean_absolute_error: 0.5099 Epoch 44/200 856/856 [==============================] - 0s 147us/sample - loss: 1.4898 - mean_squared_error: 1.4898 - mean_absolute_error: 0.9573 - val_loss: 0.3819 - val_mean_squared_error: 0.3819 - val_mean_absolute_error: 0.4605 Epoch 45/200 856/856 [==============================] - 0s 147us/sample - loss: 1.2912 - mean_squared_error: 1.2912 - mean_absolute_error: 0.9030 - val_loss: 0.4024 - val_mean_squared_error: 0.4024 - val_mean_absolute_error: 0.4775 Epoch 46/200 856/856 [==============================] - 0s 148us/sample - loss: 1.2445 - mean_squared_error: 1.2445 - mean_absolute_error: 0.8843 - val_loss: 0.4283 - val_mean_squared_error: 0.4283 - val_mean_absolute_error: 0.4954 Epoch 47/200 856/856 [==============================] - 0s 150us/sample - loss: 1.3160 - mean_squared_error: 1.3160 - mean_absolute_error: 0.9027 - val_loss: 0.3856 - val_mean_squared_error: 0.3856 - val_mean_absolute_error: 0.4654 Epoch 48/200 856/856 [==============================] - 0s 148us/sample - loss: 1.3710 - mean_squared_error: 1.3710 - mean_absolute_error: 0.9425 - val_loss: 0.3783 - val_mean_squared_error: 0.3783 - val_mean_absolute_error: 0.4569 Epoch 49/200 856/856 [==============================] - 0s 172us/sample - loss: 1.3440 - mean_squared_error: 1.3440 - mean_absolute_error: 0.9173 - val_loss: 0.4005 - val_mean_squared_error: 0.4005 - val_mean_absolute_error: 0.4715 Epoch 50/200 856/856 [==============================] - 0s 155us/sample - loss: 1.2856 - mean_squared_error: 1.2856 - mean_absolute_error: 0.8909 - val_loss: 0.3680 - val_mean_squared_error: 0.3680 - val_mean_absolute_error: 0.4483 Epoch 51/200 856/856 [==============================] - 0s 137us/sample - loss: 1.2824 - mean_squared_error: 1.2824 - mean_absolute_error: 0.8905 - val_loss: 0.3677 - val_mean_squared_error: 0.3677 - val_mean_absolute_error: 0.4505 Epoch 52/200 856/856 [==============================] - 0s 136us/sample - loss: 1.3016 - mean_squared_error: 1.3016 - mean_absolute_error: 0.8818 - val_loss: 0.3776 - val_mean_squared_error: 0.3776 - val_mean_absolute_error: 0.4561 Epoch 53/200 856/856 [==============================] - 0s 145us/sample - loss: 1.3839 - mean_squared_error: 1.3839 - mean_absolute_error: 0.9292 - val_loss: 0.4095 - val_mean_squared_error: 0.4095 - val_mean_absolute_error: 0.4743 Epoch 54/200 856/856 [==============================] - 0s 151us/sample - loss: 1.2882 - mean_squared_error: 1.2882 - mean_absolute_error: 0.8993 - val_loss: 0.3991 - val_mean_squared_error: 0.3991 - val_mean_absolute_error: 0.4744 Epoch 55/200 856/856 [==============================] - 0s 154us/sample - loss: 1.3144 - mean_squared_error: 1.3144 - mean_absolute_error: 0.9034 - val_loss: 0.3831 - val_mean_squared_error: 0.3831 - val_mean_absolute_error: 0.4613 Epoch 56/200 856/856 [==============================] - 0s 147us/sample - loss: 1.2197 - mean_squared_error: 1.2197 - mean_absolute_error: 0.8806 - val_loss: 0.3678 - val_mean_squared_error: 0.3678 - val_mean_absolute_error: 0.4494 Epoch 57/200 856/856 [==============================] - 0s 152us/sample - loss: 1.2381 - mean_squared_error: 1.2381 - mean_absolute_error: 0.8780 - val_loss: 0.3657 - val_mean_squared_error: 0.3657 - val_mean_absolute_error: 0.4507 Epoch 58/200 856/856 [==============================] - 0s 146us/sample - loss: 1.0967 - mean_squared_error: 1.0967 - mean_absolute_error: 0.8248 - val_loss: 0.4135 - val_mean_squared_error: 0.4135 - val_mean_absolute_error: 0.4777 Epoch 59/200 856/856 [==============================] - 0s 186us/sample - loss: 1.2071 - mean_squared_error: 1.2071 - mean_absolute_error: 0.8616 - val_loss: 0.3840 - val_mean_squared_error: 0.3840 - val_mean_absolute_error: 0.4601 Epoch 60/200 856/856 [==============================] - 0s 169us/sample - loss: 1.2831 - mean_squared_error: 1.2831 - mean_absolute_error: 0.8851 - val_loss: 0.3607 - val_mean_squared_error: 0.3607 - val_mean_absolute_error: 0.4491 Epoch 61/200 856/856 [==============================] - 0s 138us/sample - loss: 1.1248 - mean_squared_error: 1.1248 - mean_absolute_error: 0.8348 - val_loss: 0.3696 - val_mean_squared_error: 0.3696 - val_mean_absolute_error: 0.4585 Epoch 62/200 856/856 [==============================] - 0s 153us/sample - loss: 1.1331 - mean_squared_error: 1.1331 - mean_absolute_error: 0.8356 - val_loss: 0.3796 - val_mean_squared_error: 0.3796 - val_mean_absolute_error: 0.4742 Epoch 63/200 856/856 [==============================] - 0s 140us/sample - loss: 1.0636 - mean_squared_error: 1.0636 - mean_absolute_error: 0.8264 - val_loss: 0.3839 - val_mean_squared_error: 0.3839 - val_mean_absolute_error: 0.4722 Epoch 64/200 856/856 [==============================] - 0s 140us/sample - loss: 1.1082 - mean_squared_error: 1.1082 - mean_absolute_error: 0.8243 - val_loss: 0.3673 - val_mean_squared_error: 0.3673 - val_mean_absolute_error: 0.4637 Epoch 65/200 856/856 [==============================] - 0s 140us/sample - loss: 1.0938 - mean_squared_error: 1.0938 - mean_absolute_error: 0.8188 - val_loss: 0.3737 - val_mean_squared_error: 0.3737 - val_mean_absolute_error: 0.4624 Epoch 66/200 856/856 [==============================] - 0s 136us/sample - loss: 1.0635 - mean_squared_error: 1.0635 - mean_absolute_error: 0.8002 - val_loss: 0.3754 - val_mean_squared_error: 0.3754 - val_mean_absolute_error: 0.4659 Epoch 67/200 856/856 [==============================] - 0s 146us/sample - loss: 1.0004 - mean_squared_error: 1.0004 - mean_absolute_error: 0.7832 - val_loss: 0.3668 - val_mean_squared_error: 0.3668 - val_mean_absolute_error: 0.4605 Epoch 68/200 856/856 [==============================] - 0s 139us/sample - loss: 1.0681 - mean_squared_error: 1.0681 - mean_absolute_error: 0.7971 - val_loss: 0.3724 - val_mean_squared_error: 0.3724 - val_mean_absolute_error: 0.4584 Epoch 69/200 856/856 [==============================] - 0s 145us/sample - loss: 1.0931 - mean_squared_error: 1.0931 - mean_absolute_error: 0.8299 - val_loss: 0.3814 - val_mean_squared_error: 0.3814 - val_mean_absolute_error: 0.4607 Epoch 70/200 856/856 [==============================] - 0s 151us/sample - loss: 0.9624 - mean_squared_error: 0.9624 - mean_absolute_error: 0.7719 - val_loss: 0.3707 - val_mean_squared_error: 0.3707 - val_mean_absolute_error: 0.4597 Epoch 71/200 856/856 [==============================] - 0s 140us/sample - loss: 1.1376 - mean_squared_error: 1.1376 - mean_absolute_error: 0.8279 - val_loss: 0.3850 - val_mean_squared_error: 0.3850 - val_mean_absolute_error: 0.4732 Epoch 72/200 856/856 [==============================] - 0s 139us/sample - loss: 1.0178 - mean_squared_error: 1.0178 - mean_absolute_error: 0.8025 - val_loss: 0.3817 - val_mean_squared_error: 0.3817 - val_mean_absolute_error: 0.4678 Epoch 73/200 856/856 [==============================] - 0s 147us/sample - loss: 0.9649 - mean_squared_error: 0.9649 - mean_absolute_error: 0.7635 - val_loss: 0.4022 - val_mean_squared_error: 0.4022 - val_mean_absolute_error: 0.4810 Epoch 74/200 856/856 [==============================] - 0s 140us/sample - loss: 0.9530 - mean_squared_error: 0.9530 - mean_absolute_error: 0.7577 - val_loss: 0.4002 - val_mean_squared_error: 0.4002 - val_mean_absolute_error: 0.4838 Epoch 75/200 856/856 [==============================] - 0s 145us/sample - loss: 1.0005 - mean_squared_error: 1.0005 - mean_absolute_error: 0.7783 - val_loss: 0.3803 - val_mean_squared_error: 0.3803 - val_mean_absolute_error: 0.4792 Epoch 76/200 856/856 [==============================] - 0s 138us/sample - loss: 1.0332 - mean_squared_error: 1.0332 - mean_absolute_error: 0.7887 - val_loss: 0.3568 - val_mean_squared_error: 0.3568 - val_mean_absolute_error: 0.4648 Epoch 77/200 856/856 [==============================] - 0s 140us/sample - loss: 0.9998 - mean_squared_error: 0.9998 - mean_absolute_error: 0.7829 - val_loss: 0.3663 - val_mean_squared_error: 0.3663 - val_mean_absolute_error: 0.4636 Epoch 78/200 856/856 [==============================] - 0s 152us/sample - loss: 0.9937 - mean_squared_error: 0.9937 - mean_absolute_error: 0.7724 - val_loss: 0.3768 - val_mean_squared_error: 0.3768 - val_mean_absolute_error: 0.4645 Epoch 79/200 856/856 [==============================] - 0s 175us/sample - loss: 0.9502 - mean_squared_error: 0.9502 - mean_absolute_error: 0.7587 - val_loss: 0.3607 - val_mean_squared_error: 0.3607 - val_mean_absolute_error: 0.4539 Epoch 80/200 856/856 [==============================] - 0s 181us/sample - loss: 0.9419 - mean_squared_error: 0.9419 - mean_absolute_error: 0.7558 - val_loss: 0.3705 - val_mean_squared_error: 0.3705 - val_mean_absolute_error: 0.4657 Epoch 81/200 856/856 [==============================] - 0s 150us/sample - loss: 0.9093 - mean_squared_error: 0.9093 - mean_absolute_error: 0.7343 - val_loss: 0.3647 - val_mean_squared_error: 0.3647 - val_mean_absolute_error: 0.4599 Epoch 82/200 856/856 [==============================] - 0s 155us/sample - loss: 0.9077 - mean_squared_error: 0.9077 - mean_absolute_error: 0.7449 - val_loss: 0.3774 - val_mean_squared_error: 0.3774 - val_mean_absolute_error: 0.4597 Epoch 83/200 856/856 [==============================] - 0s 145us/sample - loss: 0.9472 - mean_squared_error: 0.9472 - mean_absolute_error: 0.7589 - val_loss: 0.3863 - val_mean_squared_error: 0.3863 - val_mean_absolute_error: 0.4749 Epoch 84/200 856/856 [==============================] - 0s 190us/sample - loss: 0.9015 - mean_squared_error: 0.9015 - mean_absolute_error: 0.7336 - val_loss: 0.3939 - val_mean_squared_error: 0.3939 - val_mean_absolute_error: 0.4852 Epoch 85/200 856/856 [==============================] - 0s 153us/sample - loss: 0.8564 - mean_squared_error: 0.8564 - mean_absolute_error: 0.7174 - val_loss: 0.3786 - val_mean_squared_error: 0.3786 - val_mean_absolute_error: 0.4748 Epoch 86/200 856/856 [==============================] - 0s 141us/sample - loss: 0.8783 - mean_squared_error: 0.8783 - mean_absolute_error: 0.7252 - val_loss: 0.3879 - val_mean_squared_error: 0.3879 - val_mean_absolute_error: 0.4779 Epoch 87/200 856/856 [==============================] - 0s 145us/sample - loss: 0.8870 - mean_squared_error: 0.8870 - mean_absolute_error: 0.7321 - val_loss: 0.3710 - val_mean_squared_error: 0.3710 - val_mean_absolute_error: 0.4663 Epoch 88/200 856/856 [==============================] - 0s 161us/sample - loss: 0.8600 - mean_squared_error: 0.8600 - mean_absolute_error: 0.7113 - val_loss: 0.3760 - val_mean_squared_error: 0.3760 - val_mean_absolute_error: 0.4750 Epoch 89/200 856/856 [==============================] - 0s 153us/sample - loss: 0.9138 - mean_squared_error: 0.9138 - mean_absolute_error: 0.7583 - val_loss: 0.3877 - val_mean_squared_error: 0.3877 - val_mean_absolute_error: 0.4834 Epoch 90/200 856/856 [==============================] - 0s 145us/sample - loss: 0.8721 - mean_squared_error: 0.8721 - mean_absolute_error: 0.7319 - val_loss: 0.3758 - val_mean_squared_error: 0.3758 - val_mean_absolute_error: 0.4680 Epoch 91/200 856/856 [==============================] - 0s 139us/sample - loss: 0.8789 - mean_squared_error: 0.8789 - mean_absolute_error: 0.7365 - val_loss: 0.4094 - val_mean_squared_error: 0.4094 - val_mean_absolute_error: 0.4984 Epoch 92/200 856/856 [==============================] - 0s 178us/sample - loss: 0.8564 - mean_squared_error: 0.8564 - mean_absolute_error: 0.7342 - val_loss: 0.3923 - val_mean_squared_error: 0.3923 - val_mean_absolute_error: 0.4870 Epoch 93/200 856/856 [==============================] - 0s 171us/sample - loss: 0.8238 - mean_squared_error: 0.8238 - mean_absolute_error: 0.7058 - val_loss: 0.3757 - val_mean_squared_error: 0.3757 - val_mean_absolute_error: 0.4748 Epoch 94/200 856/856 [==============================] - 0s 157us/sample - loss: 0.8626 - mean_squared_error: 0.8626 - mean_absolute_error: 0.7305 - val_loss: 0.3663 - val_mean_squared_error: 0.3663 - val_mean_absolute_error: 0.4693 Epoch 95/200 856/856 [==============================] - 0s 141us/sample - loss: 0.8524 - mean_squared_error: 0.8524 - mean_absolute_error: 0.7340 - val_loss: 0.3798 - val_mean_squared_error: 0.3798 - val_mean_absolute_error: 0.4778 Epoch 96/200 856/856 [==============================] - 0s 136us/sample - loss: 0.8451 - mean_squared_error: 0.8451 - mean_absolute_error: 0.7255 - val_loss: 0.3749 - val_mean_squared_error: 0.3749 - val_mean_absolute_error: 0.4734 Epoch 97/200 856/856 [==============================] - 0s 148us/sample - loss: 0.7934 - mean_squared_error: 0.7934 - mean_absolute_error: 0.7015 - val_loss: 0.3588 - val_mean_squared_error: 0.3588 - val_mean_absolute_error: 0.4664 Epoch 98/200 856/856 [==============================] - 0s 144us/sample - loss: 0.8022 - mean_squared_error: 0.8022 - mean_absolute_error: 0.6961 - val_loss: 0.3587 - val_mean_squared_error: 0.3587 - val_mean_absolute_error: 0.4682 Epoch 99/200 856/856 [==============================] - 0s 136us/sample - loss: 0.7231 - mean_squared_error: 0.7231 - mean_absolute_error: 0.6707 - val_loss: 0.3658 - val_mean_squared_error: 0.3658 - val_mean_absolute_error: 0.4698 Epoch 100/200 856/856 [==============================] - 0s 146us/sample - loss: 0.7715 - mean_squared_error: 0.7715 - mean_absolute_error: 0.6865 - val_loss: 0.3561 - val_mean_squared_error: 0.3561 - val_mean_absolute_error: 0.4710 Epoch 101/200 856/856 [==============================] - 0s 138us/sample - loss: 0.8189 - mean_squared_error: 0.8189 - mean_absolute_error: 0.7081 - val_loss: 0.3673 - val_mean_squared_error: 0.3673 - val_mean_absolute_error: 0.4715 Epoch 102/200 856/856 [==============================] - 0s 148us/sample - loss: 0.7708 - mean_squared_error: 0.7708 - mean_absolute_error: 0.6902 - val_loss: 0.3795 - val_mean_squared_error: 0.3795 - val_mean_absolute_error: 0.4794 Epoch 103/200 856/856 [==============================] - 0s 147us/sample - loss: 0.8192 - mean_squared_error: 0.8192 - mean_absolute_error: 0.7101 - val_loss: 0.3817 - val_mean_squared_error: 0.3817 - val_mean_absolute_error: 0.4792 Epoch 104/200 856/856 [==============================] - 0s 139us/sample - loss: 0.7725 - mean_squared_error: 0.7725 - mean_absolute_error: 0.6933 - val_loss: 0.3649 - val_mean_squared_error: 0.3649 - val_mean_absolute_error: 0.4712 Epoch 105/200 856/856 [==============================] - 0s 141us/sample - loss: 0.7616 - mean_squared_error: 0.7616 - mean_absolute_error: 0.6799 - val_loss: 0.3858 - val_mean_squared_error: 0.3858 - val_mean_absolute_error: 0.4860 Epoch 106/200 856/856 [==============================] - 0s 146us/sample - loss: 0.7531 - mean_squared_error: 0.7531 - mean_absolute_error: 0.6896 - val_loss: 0.3650 - val_mean_squared_error: 0.3650 - val_mean_absolute_error: 0.4796 Epoch 107/200 856/856 [==============================] - 0s 145us/sample - loss: 0.7392 - mean_squared_error: 0.7392 - mean_absolute_error: 0.6716 - val_loss: 0.3858 - val_mean_squared_error: 0.3858 - val_mean_absolute_error: 0.4812 Epoch 108/200 856/856 [==============================] - 0s 147us/sample - loss: 0.7689 - mean_squared_error: 0.7689 - mean_absolute_error: 0.6900 - val_loss: 0.3654 - val_mean_squared_error: 0.3654 - val_mean_absolute_error: 0.4776 Epoch 109/200 856/856 [==============================] - 0s 154us/sample - loss: 0.7857 - mean_squared_error: 0.7857 - mean_absolute_error: 0.6965 - val_loss: 0.3624 - val_mean_squared_error: 0.3624 - val_mean_absolute_error: 0.4734 Epoch 110/200 856/856 [==============================] - 0s 171us/sample - loss: 0.6839 - mean_squared_error: 0.6839 - mean_absolute_error: 0.6500 - val_loss: 0.3553 - val_mean_squared_error: 0.3553 - val_mean_absolute_error: 0.4660 Epoch 111/200 856/856 [==============================] - 0s 182us/sample - loss: 0.7258 - mean_squared_error: 0.7258 - mean_absolute_error: 0.6708 - val_loss: 0.3561 - val_mean_squared_error: 0.3561 - val_mean_absolute_error: 0.4642 Epoch 112/200 856/856 [==============================] - 0s 172us/sample - loss: 0.6828 - mean_squared_error: 0.6828 - mean_absolute_error: 0.6511 - val_loss: 0.3872 - val_mean_squared_error: 0.3872 - val_mean_absolute_error: 0.4867 Epoch 113/200 856/856 [==============================] - 0s 171us/sample - loss: 0.6999 - mean_squared_error: 0.6999 - mean_absolute_error: 0.6528 - val_loss: 0.3711 - val_mean_squared_error: 0.3711 - val_mean_absolute_error: 0.4816 Epoch 114/200 856/856 [==============================] - 0s 157us/sample - loss: 0.7081 - mean_squared_error: 0.7081 - mean_absolute_error: 0.6552 - val_loss: 0.3631 - val_mean_squared_error: 0.3631 - val_mean_absolute_error: 0.4720 Epoch 115/200 856/856 [==============================] - 0s 173us/sample - loss: 0.6375 - mean_squared_error: 0.6375 - mean_absolute_error: 0.6221 - val_loss: 0.3651 - val_mean_squared_error: 0.3651 - val_mean_absolute_error: 0.4752 Epoch 116/200 856/856 [==============================] - 0s 150us/sample - loss: 0.6764 - mean_squared_error: 0.6764 - mean_absolute_error: 0.6516 - val_loss: 0.3731 - val_mean_squared_error: 0.3731 - val_mean_absolute_error: 0.4818 Epoch 117/200 856/856 [==============================] - 0s 147us/sample - loss: 0.6625 - mean_squared_error: 0.6625 - mean_absolute_error: 0.6385 - val_loss: 0.3611 - val_mean_squared_error: 0.3611 - val_mean_absolute_error: 0.4751 Epoch 118/200 856/856 [==============================] - 0s 188us/sample - loss: 0.6790 - mean_squared_error: 0.6790 - mean_absolute_error: 0.6356 - val_loss: 0.3719 - val_mean_squared_error: 0.3719 - val_mean_absolute_error: 0.4794 Epoch 119/200 856/856 [==============================] - 0s 165us/sample - loss: 0.7255 - mean_squared_error: 0.7255 - mean_absolute_error: 0.6637 - val_loss: 0.3860 - val_mean_squared_error: 0.3860 - val_mean_absolute_error: 0.4866 Epoch 120/200 856/856 [==============================] - 0s 145us/sample - loss: 0.7165 - mean_squared_error: 0.7165 - mean_absolute_error: 0.6569 - val_loss: 0.3689 - val_mean_squared_error: 0.3689 - val_mean_absolute_error: 0.4783 Epoch 121/200 856/856 [==============================] - 0s 154us/sample - loss: 0.6922 - mean_squared_error: 0.6922 - mean_absolute_error: 0.6533 - val_loss: 0.3688 - val_mean_squared_error: 0.3688 - val_mean_absolute_error: 0.4722 Epoch 122/200 856/856 [==============================] - 0s 155us/sample - loss: 0.6554 - mean_squared_error: 0.6554 - mean_absolute_error: 0.6355 - val_loss: 0.3702 - val_mean_squared_error: 0.3702 - val_mean_absolute_error: 0.4706 Epoch 123/200 856/856 [==============================] - 0s 140us/sample - loss: 0.7037 - mean_squared_error: 0.7037 - mean_absolute_error: 0.6579 - val_loss: 0.3751 - val_mean_squared_error: 0.3751 - val_mean_absolute_error: 0.4684 Epoch 124/200 856/856 [==============================] - 0s 151us/sample - loss: 0.6516 - mean_squared_error: 0.6516 - mean_absolute_error: 0.6360 - val_loss: 0.3567 - val_mean_squared_error: 0.3567 - val_mean_absolute_error: 0.4707 Epoch 125/200 856/856 [==============================] - 0s 146us/sample - loss: 0.6550 - mean_squared_error: 0.6550 - mean_absolute_error: 0.6336 - val_loss: 0.3637 - val_mean_squared_error: 0.3637 - val_mean_absolute_error: 0.4734 Epoch 126/200 856/856 [==============================] - 0s 166us/sample - loss: 0.6456 - mean_squared_error: 0.6456 - mean_absolute_error: 0.6357 - val_loss: 0.3647 - val_mean_squared_error: 0.3647 - val_mean_absolute_error: 0.4741 Epoch 127/200 856/856 [==============================] - 0s 169us/sample - loss: 0.6719 - mean_squared_error: 0.6719 - mean_absolute_error: 0.6561 - val_loss: 0.3646 - val_mean_squared_error: 0.3646 - val_mean_absolute_error: 0.4775 Epoch 128/200 856/856 [==============================] - 0s 138us/sample - loss: 0.6292 - mean_squared_error: 0.6292 - mean_absolute_error: 0.6148 - val_loss: 0.3740 - val_mean_squared_error: 0.3740 - val_mean_absolute_error: 0.4847 Epoch 129/200 856/856 [==============================] - 0s 159us/sample - loss: 0.6230 - mean_squared_error: 0.6230 - mean_absolute_error: 0.6173 - val_loss: 0.3649 - val_mean_squared_error: 0.3649 - val_mean_absolute_error: 0.4806 Epoch 130/200 856/856 [==============================] - 0s 169us/sample - loss: 0.6460 - mean_squared_error: 0.6460 - mean_absolute_error: 0.6185 - val_loss: 0.3656 - val_mean_squared_error: 0.3656 - val_mean_absolute_error: 0.4786 Epoch 131/200 856/856 [==============================] - 0s 154us/sample - loss: 0.6312 - mean_squared_error: 0.6312 - mean_absolute_error: 0.6196 - val_loss: 0.3707 - val_mean_squared_error: 0.3707 - val_mean_absolute_error: 0.4817 Epoch 132/200 856/856 [==============================] - 0s 147us/sample - loss: 0.6219 - mean_squared_error: 0.6219 - mean_absolute_error: 0.6118 - val_loss: 0.3867 - val_mean_squared_error: 0.3867 - val_mean_absolute_error: 0.4874 Epoch 133/200 856/856 [==============================] - 0s 161us/sample - loss: 0.6144 - mean_squared_error: 0.6144 - mean_absolute_error: 0.6089 - val_loss: 0.3683 - val_mean_squared_error: 0.3683 - val_mean_absolute_error: 0.4793 Epoch 134/200 856/856 [==============================] - 0s 160us/sample - loss: 0.6206 - mean_squared_error: 0.6206 - mean_absolute_error: 0.6209 - val_loss: 0.3623 - val_mean_squared_error: 0.3623 - val_mean_absolute_error: 0.4720 Epoch 135/200 856/856 [==============================] - 0s 151us/sample - loss: 0.5997 - mean_squared_error: 0.5997 - mean_absolute_error: 0.6019 - val_loss: 0.3544 - val_mean_squared_error: 0.3544 - val_mean_absolute_error: 0.4702 Epoch 136/200 856/856 [==============================] - 0s 164us/sample - loss: 0.5888 - mean_squared_error: 0.5888 - mean_absolute_error: 0.6041 - val_loss: 0.3595 - val_mean_squared_error: 0.3595 - val_mean_absolute_error: 0.4729 Epoch 137/200 856/856 [==============================] - 0s 165us/sample - loss: 0.5919 - mean_squared_error: 0.5919 - mean_absolute_error: 0.5987 - val_loss: 0.3608 - val_mean_squared_error: 0.3608 - val_mean_absolute_error: 0.4734 Epoch 138/200 856/856 [==============================] - 0s 151us/sample - loss: 0.5636 - mean_squared_error: 0.5636 - mean_absolute_error: 0.5797 - val_loss: 0.3788 - val_mean_squared_error: 0.3788 - val_mean_absolute_error: 0.4864 Epoch 139/200 856/856 [==============================] - 0s 158us/sample - loss: 0.5977 - mean_squared_error: 0.5977 - mean_absolute_error: 0.6006 - val_loss: 0.3748 - val_mean_squared_error: 0.3748 - val_mean_absolute_error: 0.4814 Epoch 140/200 856/856 [==============================] - 0s 161us/sample - loss: 0.6004 - mean_squared_error: 0.6004 - mean_absolute_error: 0.6120 - val_loss: 0.3638 - val_mean_squared_error: 0.3638 - val_mean_absolute_error: 0.4761 Epoch 141/200 856/856 [==============================] - 0s 159us/sample - loss: 0.6116 - mean_squared_error: 0.6116 - mean_absolute_error: 0.6077 - val_loss: 0.3647 - val_mean_squared_error: 0.3647 - val_mean_absolute_error: 0.4753 Epoch 142/200 856/856 [==============================] - 0s 154us/sample - loss: 0.5804 - mean_squared_error: 0.5804 - mean_absolute_error: 0.5895 - val_loss: 0.3599 - val_mean_squared_error: 0.3599 - val_mean_absolute_error: 0.4749 Epoch 143/200 856/856 [==============================] - 0s 154us/sample - loss: 0.5907 - mean_squared_error: 0.5907 - mean_absolute_error: 0.6013 - val_loss: 0.3730 - val_mean_squared_error: 0.3730 - val_mean_absolute_error: 0.4829 Epoch 144/200 856/856 [==============================] - 0s 158us/sample - loss: 0.5598 - mean_squared_error: 0.5598 - mean_absolute_error: 0.5767 - val_loss: 0.3635 - val_mean_squared_error: 0.3635 - val_mean_absolute_error: 0.4794 Epoch 145/200 856/856 [==============================] - 0s 150us/sample - loss: 0.5963 - mean_squared_error: 0.5963 - mean_absolute_error: 0.5988 - val_loss: 0.3628 - val_mean_squared_error: 0.3628 - val_mean_absolute_error: 0.4758 Epoch 146/200 856/856 [==============================] - 0s 139us/sample - loss: 0.5703 - mean_squared_error: 0.5703 - mean_absolute_error: 0.5910 - val_loss: 0.3549 - val_mean_squared_error: 0.3549 - val_mean_absolute_error: 0.4717 Epoch 147/200 856/856 [==============================] - 0s 139us/sample - loss: 0.5442 - mean_squared_error: 0.5442 - mean_absolute_error: 0.5774 - val_loss: 0.3645 - val_mean_squared_error: 0.3645 - val_mean_absolute_error: 0.4740 Epoch 148/200 856/856 [==============================] - 0s 145us/sample - loss: 0.5716 - mean_squared_error: 0.5716 - mean_absolute_error: 0.5908 - val_loss: 0.3645 - val_mean_squared_error: 0.3645 - val_mean_absolute_error: 0.4781 Epoch 149/200 856/856 [==============================] - 0s 146us/sample - loss: 0.5817 - mean_squared_error: 0.5817 - mean_absolute_error: 0.6010 - val_loss: 0.3619 - val_mean_squared_error: 0.3619 - val_mean_absolute_error: 0.4784 Epoch 150/200 856/856 [==============================] - 0s 138us/sample - loss: 0.5815 - mean_squared_error: 0.5815 - mean_absolute_error: 0.6028 - val_loss: 0.3536 - val_mean_squared_error: 0.3536 - val_mean_absolute_error: 0.4722 Epoch 151/200 856/856 [==============================] - 0s 143us/sample - loss: 0.5714 - mean_squared_error: 0.5714 - mean_absolute_error: 0.5883 - val_loss: 0.3578 - val_mean_squared_error: 0.3578 - val_mean_absolute_error: 0.4738 Epoch 152/200 856/856 [==============================] - 0s 145us/sample - loss: 0.5688 - mean_squared_error: 0.5688 - mean_absolute_error: 0.5847 - val_loss: 0.3553 - val_mean_squared_error: 0.3553 - val_mean_absolute_error: 0.4704 Epoch 153/200 856/856 [==============================] - 0s 150us/sample - loss: 0.5589 - mean_squared_error: 0.5589 - mean_absolute_error: 0.5854 - val_loss: 0.3736 - val_mean_squared_error: 0.3736 - val_mean_absolute_error: 0.4812 Epoch 154/200 856/856 [==============================] - 0s 147us/sample - loss: 0.5790 - mean_squared_error: 0.5790 - mean_absolute_error: 0.5883 - val_loss: 0.3843 - val_mean_squared_error: 0.3843 - val_mean_absolute_error: 0.4889 Epoch 155/200 856/856 [==============================] - 0s 141us/sample - loss: 0.5578 - mean_squared_error: 0.5578 - mean_absolute_error: 0.5794 - val_loss: 0.3538 - val_mean_squared_error: 0.3538 - val_mean_absolute_error: 0.4720 Epoch 156/200 856/856 [==============================] - 0s 151us/sample - loss: 0.5610 - mean_squared_error: 0.5610 - mean_absolute_error: 0.5843 - val_loss: 0.3605 - val_mean_squared_error: 0.3605 - val_mean_absolute_error: 0.4722 Epoch 157/200 856/856 [==============================] - 0s 151us/sample - loss: 0.5144 - mean_squared_error: 0.5144 - mean_absolute_error: 0.5602 - val_loss: 0.3713 - val_mean_squared_error: 0.3713 - val_mean_absolute_error: 0.4811 Epoch 158/200 856/856 [==============================] - 0s 148us/sample - loss: 0.5269 - mean_squared_error: 0.5269 - mean_absolute_error: 0.5670 - val_loss: 0.3542 - val_mean_squared_error: 0.3542 - val_mean_absolute_error: 0.4709 Epoch 159/200 856/856 [==============================] - 0s 144us/sample - loss: 0.5238 - mean_squared_error: 0.5238 - mean_absolute_error: 0.5627 - val_loss: 0.3564 - val_mean_squared_error: 0.3564 - val_mean_absolute_error: 0.4673 Epoch 160/200 856/856 [==============================] - 0s 154us/sample - loss: 0.5158 - mean_squared_error: 0.5158 - mean_absolute_error: 0.5597 - val_loss: 0.3656 - val_mean_squared_error: 0.3656 - val_mean_absolute_error: 0.4765 Epoch 161/200 856/856 [==============================] - 0s 151us/sample - loss: 0.5406 - mean_squared_error: 0.5406 - mean_absolute_error: 0.5681 - val_loss: 0.3546 - val_mean_squared_error: 0.3546 - val_mean_absolute_error: 0.4721 Epoch 162/200 856/856 [==============================] - 0s 172us/sample - loss: 0.5272 - mean_squared_error: 0.5272 - mean_absolute_error: 0.5708 - val_loss: 0.3606 - val_mean_squared_error: 0.3606 - val_mean_absolute_error: 0.4777 Epoch 163/200 856/856 [==============================] - 0s 154us/sample - loss: 0.5283 - mean_squared_error: 0.5283 - mean_absolute_error: 0.5707 - val_loss: 0.3603 - val_mean_squared_error: 0.3603 - val_mean_absolute_error: 0.4750 Epoch 164/200 856/856 [==============================] - 0s 153us/sample - loss: 0.5102 - mean_squared_error: 0.5102 - mean_absolute_error: 0.5588 - val_loss: 0.3702 - val_mean_squared_error: 0.3702 - val_mean_absolute_error: 0.4791 Epoch 165/200 856/856 [==============================] - 0s 185us/sample - loss: 0.5257 - mean_squared_error: 0.5257 - mean_absolute_error: 0.5630 - val_loss: 0.3642 - val_mean_squared_error: 0.3642 - val_mean_absolute_error: 0.4794 Epoch 166/200 856/856 [==============================] - 0s 139us/sample - loss: 0.4972 - mean_squared_error: 0.4972 - mean_absolute_error: 0.5546 - val_loss: 0.3559 - val_mean_squared_error: 0.3559 - val_mean_absolute_error: 0.4712 Epoch 167/200 856/856 [==============================] - 0s 139us/sample - loss: 0.5338 - mean_squared_error: 0.5338 - mean_absolute_error: 0.5655 - val_loss: 0.3598 - val_mean_squared_error: 0.3598 - val_mean_absolute_error: 0.4733 Epoch 168/200 856/856 [==============================] - 0s 146us/sample - loss: 0.5335 - mean_squared_error: 0.5335 - mean_absolute_error: 0.5657 - val_loss: 0.3596 - val_mean_squared_error: 0.3596 - val_mean_absolute_error: 0.4742 Epoch 169/200 856/856 [==============================] - 0s 148us/sample - loss: 0.5251 - mean_squared_error: 0.5251 - mean_absolute_error: 0.5655 - val_loss: 0.3603 - val_mean_squared_error: 0.3603 - val_mean_absolute_error: 0.4723 Epoch 170/200 856/856 [==============================] - 0s 150us/sample - loss: 0.5126 - mean_squared_error: 0.5126 - mean_absolute_error: 0.5653 - val_loss: 0.3614 - val_mean_squared_error: 0.3614 - val_mean_absolute_error: 0.4729 Epoch 171/200 856/856 [==============================] - 0s 155us/sample - loss: 0.5175 - mean_squared_error: 0.5175 - mean_absolute_error: 0.5553 - val_loss: 0.3669 - val_mean_squared_error: 0.3669 - val_mean_absolute_error: 0.4795 Epoch 172/200 856/856 [==============================] - 0s 147us/sample - loss: 0.5388 - mean_squared_error: 0.5388 - mean_absolute_error: 0.5732 - val_loss: 0.3700 - val_mean_squared_error: 0.3700 - val_mean_absolute_error: 0.4832 Epoch 173/200 856/856 [==============================] - 0s 148us/sample - loss: 0.5274 - mean_squared_error: 0.5274 - mean_absolute_error: 0.5668 - val_loss: 0.3590 - val_mean_squared_error: 0.3590 - val_mean_absolute_error: 0.4745 Epoch 174/200 856/856 [==============================] - 0s 148us/sample - loss: 0.5219 - mean_squared_error: 0.5219 - mean_absolute_error: 0.5676 - val_loss: 0.3578 - val_mean_squared_error: 0.3578 - val_mean_absolute_error: 0.4729 Epoch 175/200 856/856 [==============================] - 0s 138us/sample - loss: 0.5514 - mean_squared_error: 0.5514 - mean_absolute_error: 0.5749 - val_loss: 0.3668 - val_mean_squared_error: 0.3668 - val_mean_absolute_error: 0.4812 Epoch 176/200 856/856 [==============================] - 0s 144us/sample - loss: 0.5240 - mean_squared_error: 0.5240 - mean_absolute_error: 0.5750 - val_loss: 0.3601 - val_mean_squared_error: 0.3601 - val_mean_absolute_error: 0.4755 Epoch 177/200 856/856 [==============================] - 0s 145us/sample - loss: 0.5102 - mean_squared_error: 0.5102 - mean_absolute_error: 0.5582 - val_loss: 0.3645 - val_mean_squared_error: 0.3645 - val_mean_absolute_error: 0.4789 Epoch 178/200 856/856 [==============================] - 0s 134us/sample - loss: 0.5091 - mean_squared_error: 0.5091 - mean_absolute_error: 0.5605 - val_loss: 0.3592 - val_mean_squared_error: 0.3592 - val_mean_absolute_error: 0.4763 Epoch 179/200 856/856 [==============================] - 0s 145us/sample - loss: 0.5197 - mean_squared_error: 0.5197 - mean_absolute_error: 0.5715 - val_loss: 0.3603 - val_mean_squared_error: 0.3603 - val_mean_absolute_error: 0.4775 Epoch 180/200 856/856 [==============================] - 0s 145us/sample - loss: 0.4918 - mean_squared_error: 0.4918 - mean_absolute_error: 0.5496 - val_loss: 0.3586 - val_mean_squared_error: 0.3586 - val_mean_absolute_error: 0.4743 Epoch 181/200 856/856 [==============================] - 0s 138us/sample - loss: 0.5166 - mean_squared_error: 0.5166 - mean_absolute_error: 0.5670 - val_loss: 0.3573 - val_mean_squared_error: 0.3573 - val_mean_absolute_error: 0.4728 Epoch 182/200 856/856 [==============================] - 0s 147us/sample - loss: 0.4981 - mean_squared_error: 0.4981 - mean_absolute_error: 0.5531 - val_loss: 0.3543 - val_mean_squared_error: 0.3543 - val_mean_absolute_error: 0.4714 Epoch 183/200 856/856 [==============================] - 0s 152us/sample - loss: 0.4916 - mean_squared_error: 0.4916 - mean_absolute_error: 0.5530 - val_loss: 0.3557 - val_mean_squared_error: 0.3557 - val_mean_absolute_error: 0.4736 Epoch 184/200 856/856 [==============================] - 0s 147us/sample - loss: 0.5209 - mean_squared_error: 0.5209 - mean_absolute_error: 0.5701 - val_loss: 0.3646 - val_mean_squared_error: 0.3646 - val_mean_absolute_error: 0.4801 Epoch 185/200 856/856 [==============================] - 0s 143us/sample - loss: 0.5306 - mean_squared_error: 0.5306 - mean_absolute_error: 0.5620 - val_loss: 0.3590 - val_mean_squared_error: 0.3590 - val_mean_absolute_error: 0.4755 Epoch 186/200 856/856 [==============================] - 0s 154us/sample - loss: 0.4884 - mean_squared_error: 0.4884 - mean_absolute_error: 0.5489 - val_loss: 0.3669 - val_mean_squared_error: 0.3669 - val_mean_absolute_error: 0.4824 Epoch 187/200 856/856 [==============================] - 0s 146us/sample - loss: 0.5013 - mean_squared_error: 0.5013 - mean_absolute_error: 0.5569 - val_loss: 0.3546 - val_mean_squared_error: 0.3546 - val_mean_absolute_error: 0.4722 Epoch 188/200 856/856 [==============================] - 0s 138us/sample - loss: 0.4951 - mean_squared_error: 0.4951 - mean_absolute_error: 0.5482 - val_loss: 0.3637 - val_mean_squared_error: 0.3637 - val_mean_absolute_error: 0.4813 Epoch 189/200 856/856 [==============================] - 0s 162us/sample - loss: 0.5112 - mean_squared_error: 0.5112 - mean_absolute_error: 0.5627 - val_loss: 0.3581 - val_mean_squared_error: 0.3581 - val_mean_absolute_error: 0.4761 Epoch 190/200 856/856 [==============================] - 0s 176us/sample - loss: 0.5144 - mean_squared_error: 0.5144 - mean_absolute_error: 0.5701 - val_loss: 0.3616 - val_mean_squared_error: 0.3616 - val_mean_absolute_error: 0.4772 Epoch 191/200 856/856 [==============================] - 0s 152us/sample - loss: 0.5125 - mean_squared_error: 0.5125 - mean_absolute_error: 0.5607 - val_loss: 0.3730 - val_mean_squared_error: 0.3730 - val_mean_absolute_error: 0.4886 Epoch 192/200 856/856 [==============================] - 0s 147us/sample - loss: 0.5111 - mean_squared_error: 0.5111 - mean_absolute_error: 0.5581 - val_loss: 0.3665 - val_mean_squared_error: 0.3665 - val_mean_absolute_error: 0.4834 Epoch 193/200 856/856 [==============================] - 0s 151us/sample - loss: 0.5090 - mean_squared_error: 0.5090 - mean_absolute_error: 0.5600 - val_loss: 0.3586 - val_mean_squared_error: 0.3586 - val_mean_absolute_error: 0.4763 Epoch 194/200 856/856 [==============================] - 0s 155us/sample - loss: 0.4744 - mean_squared_error: 0.4744 - mean_absolute_error: 0.5444 - val_loss: 0.3598 - val_mean_squared_error: 0.3598 - val_mean_absolute_error: 0.4767 Epoch 195/200 856/856 [==============================] - 0s 155us/sample - loss: 0.5139 - mean_squared_error: 0.5139 - mean_absolute_error: 0.5652 - val_loss: 0.3643 - val_mean_squared_error: 0.3643 - val_mean_absolute_error: 0.4809 Epoch 196/200 856/856 [==============================] - 0s 154us/sample - loss: 0.4856 - mean_squared_error: 0.4856 - mean_absolute_error: 0.5465 - val_loss: 0.3589 - val_mean_squared_error: 0.3589 - val_mean_absolute_error: 0.4763 Epoch 197/200 856/856 [==============================] - 0s 157us/sample - loss: 0.5062 - mean_squared_error: 0.5062 - mean_absolute_error: 0.5616 - val_loss: 0.3602 - val_mean_squared_error: 0.3602 - val_mean_absolute_error: 0.4783 Epoch 198/200 856/856 [==============================] - 0s 162us/sample - loss: 0.4805 - mean_squared_error: 0.4805 - mean_absolute_error: 0.5557 - val_loss: 0.3602 - val_mean_squared_error: 0.3602 - val_mean_absolute_error: 0.4763 Epoch 199/200 856/856 [==============================] - 0s 144us/sample - loss: 0.4877 - mean_squared_error: 0.4877 - mean_absolute_error: 0.5545 - val_loss: 0.3620 - val_mean_squared_error: 0.3620 - val_mean_absolute_error: 0.4774 Epoch 200/200 856/856 [==============================] - 0s 140us/sample - loss: 0.5055 - mean_squared_error: 0.5055 - mean_absolute_error: 0.5624 - val_loss: 0.3683 - val_mean_squared_error: 0.3683 - val_mean_absolute_error: 0.4835
print(history.history.keys())
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
dict_keys(['loss', 'mean_squared_error', 'mean_absolute_error', 'val_loss', 'val_mean_squared_error', 'val_mean_absolute_error'])
y_pred = model.predict(X_test)
mean_squared_error(y_test, y_pred)
0.46068788382694303
Observations:
for column in After_backward_elimination.drop(columns=['Signal_Strength']).columns:
After_backward_elimination[column] = After_backward_elimination[column].apply(lambda x:
(x - After_backward_elimination[column].mean())/After_backward_elimination[column].std()
)
After_backward_elimination
| Parameter 2 | Parameter 5 | Parameter 7 | Parameter 8 | Parameter 9 | Parameter 10 | Parameter 11 | Signal_Strength | |
|---|---|---|---|---|---|---|---|---|
| 0 | 0.961576 | -0.243630 | -0.379014 | 0.558100 | 1.288240 | -0.579025 | -0.959946 | 5 |
| 1 | 1.966827 | 0.223805 | 0.624168 | 0.028252 | -0.719708 | 0.128910 | -0.584594 | 5 |
| 2 | 1.296660 | 0.096323 | 0.228975 | 0.134222 | -0.331073 | -0.048074 | -0.584594 | 5 |
| 3 | -1.384011 | -0.264878 | 0.411372 | 0.664069 | -0.978798 | -0.461036 | -0.584594 | 6 |
| 4 | 0.961576 | -0.243630 | -0.379014 | 0.558100 | 1.288240 | -0.579025 | -0.959946 | 5 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 1594 | 0.403103 | 0.053829 | -0.075020 | -0.978459 | 0.899605 | -0.461036 | 0.072271 | 5 |
| 1595 | 0.123866 | -0.541090 | 0.137777 | -0.861893 | 1.353012 | 0.600867 | 0.729136 | 6 |
| 1596 | -0.099523 | -0.243630 | -0.196617 | -0.533387 | 0.705287 | 0.541872 | 0.541460 | 6 |
| 1597 | 0.654416 | -0.264878 | -0.075020 | -0.676446 | 1.676875 | 0.305894 | -0.209243 | 5 |
| 1598 | -1.216469 | -0.434854 | -0.135818 | -0.665849 | 0.510970 | 0.010921 | 0.541460 | 6 |
1599 rows × 8 columns
X = After_backward_elimination.drop(columns=['Signal_Strength']).values
Y = After_backward_elimination['Signal_Strength'].values
X_train, X_test, y_train, y_test = train_test_split(X, Y,test_size=0.33, random_state=42)
X.shape
(1599, 7)
model1 = Sequential()
model1.add(Dense(10, input_dim=7, kernel_initializer='normal', activation='relu'))
model1.add(Dropout(0.25))
model1.add(Dense(10, activation='relu'))
model1.add(Dropout(0.25))
model1.add(Dense(1, activation='linear'))
model1.summary()
Model: "sequential_19" _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= dense_58 (Dense) (None, 10) 80 _________________________________________________________________ dropout_32 (Dropout) (None, 10) 0 _________________________________________________________________ dense_59 (Dense) (None, 10) 110 _________________________________________________________________ dropout_33 (Dropout) (None, 10) 0 _________________________________________________________________ dense_60 (Dense) (None, 1) 11 ================================================================= Total params: 201 Trainable params: 201 Non-trainable params: 0 _________________________________________________________________
model1.compile(loss='mse', optimizer='adam', metrics=['mse','mae'])
history = model1.fit(X_train, y_train, epochs=200, batch_size=10, verbose=1, validation_split=0.2)
Train on 856 samples, validate on 215 samples Epoch 1/200 856/856 [==============================] - 1s 713us/sample - loss: 27.0769 - mean_squared_error: 27.0769 - mean_absolute_error: 5.1202 - val_loss: 21.0025 - val_mean_squared_error: 21.0025 - val_mean_absolute_error: 4.5084 Epoch 2/200 856/856 [==============================] - 0s 153us/sample - loss: 14.2819 - mean_squared_error: 14.2819 - mean_absolute_error: 3.5655 - val_loss: 6.6728 - val_mean_squared_error: 6.6728 - val_mean_absolute_error: 2.4240 Epoch 3/200 856/856 [==============================] - 0s 157us/sample - loss: 5.8012 - mean_squared_error: 5.8012 - mean_absolute_error: 2.0230 - val_loss: 1.7196 - val_mean_squared_error: 1.7196 - val_mean_absolute_error: 1.1247 Epoch 4/200 856/856 [==============================] - 0s 166us/sample - loss: 3.6995 - mean_squared_error: 3.6995 - mean_absolute_error: 1.5689 - val_loss: 0.9809 - val_mean_squared_error: 0.9809 - val_mean_absolute_error: 0.7907 Epoch 5/200 856/856 [==============================] - 0s 158us/sample - loss: 3.4464 - mean_squared_error: 3.4464 - mean_absolute_error: 1.4866 - val_loss: 0.8083 - val_mean_squared_error: 0.8083 - val_mean_absolute_error: 0.7176 Epoch 6/200 856/856 [==============================] - 0s 158us/sample - loss: 3.4701 - mean_squared_error: 3.4701 - mean_absolute_error: 1.4903 - val_loss: 0.7205 - val_mean_squared_error: 0.7205 - val_mean_absolute_error: 0.6643 Epoch 7/200 856/856 [==============================] - 0s 159us/sample - loss: 3.0271 - mean_squared_error: 3.0271 - mean_absolute_error: 1.3829 - val_loss: 0.6496 - val_mean_squared_error: 0.6496 - val_mean_absolute_error: 0.6268 Epoch 8/200 856/856 [==============================] - 0s 157us/sample - loss: 2.6860 - mean_squared_error: 2.6860 - mean_absolute_error: 1.3193 - val_loss: 0.5993 - val_mean_squared_error: 0.5993 - val_mean_absolute_error: 0.6042 Epoch 9/200 856/856 [==============================] - 0s 164us/sample - loss: 2.5391 - mean_squared_error: 2.5391 - mean_absolute_error: 1.2836 - val_loss: 0.4838 - val_mean_squared_error: 0.4838 - val_mean_absolute_error: 0.5333 Epoch 10/200 856/856 [==============================] - 0s 158us/sample - loss: 2.5447 - mean_squared_error: 2.5447 - mean_absolute_error: 1.2809 - val_loss: 0.5368 - val_mean_squared_error: 0.5368 - val_mean_absolute_error: 0.5804 Epoch 11/200 856/856 [==============================] - 0s 162us/sample - loss: 2.5914 - mean_squared_error: 2.5914 - mean_absolute_error: 1.2881 - val_loss: 0.4532 - val_mean_squared_error: 0.4532 - val_mean_absolute_error: 0.5244 Epoch 12/200 856/856 [==============================] - 0s 165us/sample - loss: 2.5336 - mean_squared_error: 2.5336 - mean_absolute_error: 1.2786 - val_loss: 0.4985 - val_mean_squared_error: 0.4985 - val_mean_absolute_error: 0.5529 Epoch 13/200 856/856 [==============================] - 0s 151us/sample - loss: 2.4436 - mean_squared_error: 2.4436 - mean_absolute_error: 1.2506 - val_loss: 0.5087 - val_mean_squared_error: 0.5087 - val_mean_absolute_error: 0.5666 Epoch 14/200 856/856 [==============================] - 0s 158us/sample - loss: 2.2224 - mean_squared_error: 2.2224 - mean_absolute_error: 1.1857 - val_loss: 0.4428 - val_mean_squared_error: 0.4428 - val_mean_absolute_error: 0.5094 Epoch 15/200 856/856 [==============================] - 0s 160us/sample - loss: 2.4097 - mean_squared_error: 2.4097 - mean_absolute_error: 1.2289 - val_loss: 0.4791 - val_mean_squared_error: 0.4791 - val_mean_absolute_error: 0.5405 Epoch 16/200 856/856 [==============================] - 0s 159us/sample - loss: 2.1911 - mean_squared_error: 2.1911 - mean_absolute_error: 1.1879 - val_loss: 0.5286 - val_mean_squared_error: 0.5286 - val_mean_absolute_error: 0.5771 Epoch 17/200 856/856 [==============================] - 0s 148us/sample - loss: 2.1595 - mean_squared_error: 2.1595 - mean_absolute_error: 1.1797 - val_loss: 0.5149 - val_mean_squared_error: 0.5149 - val_mean_absolute_error: 0.5585 Epoch 18/200 856/856 [==============================] - 0s 155us/sample - loss: 2.1089 - mean_squared_error: 2.1089 - mean_absolute_error: 1.1614 - val_loss: 0.4248 - val_mean_squared_error: 0.4248 - val_mean_absolute_error: 0.4939 Epoch 19/200 856/856 [==============================] - 0s 175us/sample - loss: 2.1095 - mean_squared_error: 2.1095 - mean_absolute_error: 1.1711 - val_loss: 0.4763 - val_mean_squared_error: 0.4763 - val_mean_absolute_error: 0.5404 Epoch 20/200 856/856 [==============================] - 0s 169us/sample - loss: 2.0930 - mean_squared_error: 2.0930 - mean_absolute_error: 1.1383 - val_loss: 0.4934 - val_mean_squared_error: 0.4934 - val_mean_absolute_error: 0.5390 Epoch 21/200 856/856 [==============================] - 0s 169us/sample - loss: 2.0752 - mean_squared_error: 2.0752 - mean_absolute_error: 1.1282 - val_loss: 0.4523 - val_mean_squared_error: 0.4523 - val_mean_absolute_error: 0.5144 Epoch 22/200 856/856 [==============================] - 0s 222us/sample - loss: 1.9387 - mean_squared_error: 1.9387 - mean_absolute_error: 1.1009 - val_loss: 0.4624 - val_mean_squared_error: 0.4624 - val_mean_absolute_error: 0.5183 Epoch 23/200 856/856 [==============================] - 0s 181us/sample - loss: 1.8747 - mean_squared_error: 1.8747 - mean_absolute_error: 1.0867 - val_loss: 0.3506 - val_mean_squared_error: 0.3506 - val_mean_absolute_error: 0.4441 Epoch 24/200 856/856 [==============================] - 0s 155us/sample - loss: 1.7527 - mean_squared_error: 1.7527 - mean_absolute_error: 1.0586 - val_loss: 0.4554 - val_mean_squared_error: 0.4554 - val_mean_absolute_error: 0.5172 Epoch 25/200 856/856 [==============================] - 0s 162us/sample - loss: 1.7567 - mean_squared_error: 1.7567 - mean_absolute_error: 1.0541 - val_loss: 0.4554 - val_mean_squared_error: 0.4554 - val_mean_absolute_error: 0.5285 Epoch 26/200 856/856 [==============================] - 0s 174us/sample - loss: 1.6403 - mean_squared_error: 1.6403 - mean_absolute_error: 1.0020 - val_loss: 0.3817 - val_mean_squared_error: 0.3817 - val_mean_absolute_error: 0.4671 Epoch 27/200 856/856 [==============================] - 0s 162us/sample - loss: 1.8577 - mean_squared_error: 1.8577 - mean_absolute_error: 1.0681 - val_loss: 0.4780 - val_mean_squared_error: 0.4780 - val_mean_absolute_error: 0.5300 Epoch 28/200 856/856 [==============================] - 0s 153us/sample - loss: 1.6038 - mean_squared_error: 1.6038 - mean_absolute_error: 0.9878 - val_loss: 0.4114 - val_mean_squared_error: 0.4114 - val_mean_absolute_error: 0.4873 Epoch 29/200 856/856 [==============================] - 0s 192us/sample - loss: 1.6754 - mean_squared_error: 1.6754 - mean_absolute_error: 1.0245 - val_loss: 0.3951 - val_mean_squared_error: 0.3951 - val_mean_absolute_error: 0.4748 Epoch 30/200 856/856 [==============================] - 0s 166us/sample - loss: 1.6808 - mean_squared_error: 1.6808 - mean_absolute_error: 1.0188 - val_loss: 0.4320 - val_mean_squared_error: 0.4320 - val_mean_absolute_error: 0.4989 Epoch 31/200 856/856 [==============================] - 0s 165us/sample - loss: 1.6089 - mean_squared_error: 1.6089 - mean_absolute_error: 0.9751 - val_loss: 0.4059 - val_mean_squared_error: 0.4059 - val_mean_absolute_error: 0.4845 Epoch 32/200 856/856 [==============================] - 0s 183us/sample - loss: 1.6332 - mean_squared_error: 1.6332 - mean_absolute_error: 1.0078 - val_loss: 0.3744 - val_mean_squared_error: 0.3744 - val_mean_absolute_error: 0.4632 Epoch 33/200 856/856 [==============================] - 0s 155us/sample - loss: 1.5424 - mean_squared_error: 1.5424 - mean_absolute_error: 0.9934 - val_loss: 0.3782 - val_mean_squared_error: 0.3782 - val_mean_absolute_error: 0.4674 Epoch 34/200 856/856 [==============================] - 0s 181us/sample - loss: 1.6157 - mean_squared_error: 1.6157 - mean_absolute_error: 0.9932 - val_loss: 0.3925 - val_mean_squared_error: 0.3925 - val_mean_absolute_error: 0.4741 Epoch 35/200 856/856 [==============================] - 0s 159us/sample - loss: 1.5511 - mean_squared_error: 1.5511 - mean_absolute_error: 0.9932 - val_loss: 0.3517 - val_mean_squared_error: 0.3517 - val_mean_absolute_error: 0.4419 Epoch 36/200 856/856 [==============================] - 0s 153us/sample - loss: 1.5220 - mean_squared_error: 1.5220 - mean_absolute_error: 0.9721 - val_loss: 0.3852 - val_mean_squared_error: 0.3852 - val_mean_absolute_error: 0.4673 Epoch 37/200 856/856 [==============================] - 0s 137us/sample - loss: 1.5409 - mean_squared_error: 1.5409 - mean_absolute_error: 0.9872 - val_loss: 0.3771 - val_mean_squared_error: 0.3771 - val_mean_absolute_error: 0.4640 Epoch 38/200 856/856 [==============================] - 0s 139us/sample - loss: 1.5172 - mean_squared_error: 1.5172 - mean_absolute_error: 0.9738 - val_loss: 0.3866 - val_mean_squared_error: 0.3866 - val_mean_absolute_error: 0.4750 Epoch 39/200 856/856 [==============================] - 0s 141us/sample - loss: 1.4369 - mean_squared_error: 1.4369 - mean_absolute_error: 0.9417 - val_loss: 0.3820 - val_mean_squared_error: 0.3820 - val_mean_absolute_error: 0.4695 Epoch 40/200 856/856 [==============================] - 0s 146us/sample - loss: 1.4758 - mean_squared_error: 1.4758 - mean_absolute_error: 0.9479 - val_loss: 0.3766 - val_mean_squared_error: 0.3766 - val_mean_absolute_error: 0.4643 Epoch 41/200 856/856 [==============================] - 0s 174us/sample - loss: 1.3019 - mean_squared_error: 1.3019 - mean_absolute_error: 0.8998 - val_loss: 0.3973 - val_mean_squared_error: 0.3973 - val_mean_absolute_error: 0.4845 Epoch 42/200 856/856 [==============================] - 0s 172us/sample - loss: 1.3523 - mean_squared_error: 1.3523 - mean_absolute_error: 0.9095 - val_loss: 0.3887 - val_mean_squared_error: 0.3887 - val_mean_absolute_error: 0.4737 Epoch 43/200 856/856 [==============================] - 0s 144us/sample - loss: 1.4531 - mean_squared_error: 1.4531 - mean_absolute_error: 0.9650 - val_loss: 0.3811 - val_mean_squared_error: 0.3811 - val_mean_absolute_error: 0.4682 Epoch 44/200 856/856 [==============================] - 0s 180us/sample - loss: 1.4338 - mean_squared_error: 1.4338 - mean_absolute_error: 0.9579 - val_loss: 0.3536 - val_mean_squared_error: 0.3536 - val_mean_absolute_error: 0.4512 Epoch 45/200 856/856 [==============================] - 0s 188us/sample - loss: 1.3920 - mean_squared_error: 1.3920 - mean_absolute_error: 0.9272 - val_loss: 0.3738 - val_mean_squared_error: 0.3738 - val_mean_absolute_error: 0.4605 Epoch 46/200 856/856 [==============================] - 0s 154us/sample - loss: 1.4071 - mean_squared_error: 1.4071 - mean_absolute_error: 0.9307 - val_loss: 0.3715 - val_mean_squared_error: 0.3715 - val_mean_absolute_error: 0.4594 Epoch 47/200 856/856 [==============================] - 0s 193us/sample - loss: 1.2651 - mean_squared_error: 1.2651 - mean_absolute_error: 0.8719 - val_loss: 0.3524 - val_mean_squared_error: 0.3524 - val_mean_absolute_error: 0.4471 Epoch 48/200 856/856 [==============================] - 0s 138us/sample - loss: 1.3630 - mean_squared_error: 1.3630 - mean_absolute_error: 0.9229 - val_loss: 0.3631 - val_mean_squared_error: 0.3631 - val_mean_absolute_error: 0.4487 Epoch 49/200 856/856 [==============================] - 0s 150us/sample - loss: 1.2453 - mean_squared_error: 1.2453 - mean_absolute_error: 0.8896 - val_loss: 0.3463 - val_mean_squared_error: 0.3463 - val_mean_absolute_error: 0.4383 Epoch 50/200 856/856 [==============================] - 0s 151us/sample - loss: 1.3090 - mean_squared_error: 1.3090 - mean_absolute_error: 0.9063 - val_loss: 0.3582 - val_mean_squared_error: 0.3582 - val_mean_absolute_error: 0.4460 Epoch 51/200 856/856 [==============================] - 0s 148us/sample - loss: 1.1917 - mean_squared_error: 1.1917 - mean_absolute_error: 0.8614 - val_loss: 0.3440 - val_mean_squared_error: 0.3440 - val_mean_absolute_error: 0.4422 Epoch 52/200 856/856 [==============================] - 0s 141us/sample - loss: 1.2214 - mean_squared_error: 1.2214 - mean_absolute_error: 0.8671 - val_loss: 0.3467 - val_mean_squared_error: 0.3467 - val_mean_absolute_error: 0.4439 Epoch 53/200 856/856 [==============================] - 0s 148us/sample - loss: 1.2467 - mean_squared_error: 1.2467 - mean_absolute_error: 0.8850 - val_loss: 0.3681 - val_mean_squared_error: 0.3681 - val_mean_absolute_error: 0.4513 Epoch 54/200 856/856 [==============================] - 0s 148us/sample - loss: 1.2884 - mean_squared_error: 1.2884 - mean_absolute_error: 0.8921 - val_loss: 0.4086 - val_mean_squared_error: 0.4086 - val_mean_absolute_error: 0.4812 Epoch 55/200 856/856 [==============================] - 0s 140us/sample - loss: 1.2801 - mean_squared_error: 1.2801 - mean_absolute_error: 0.8881 - val_loss: 0.3907 - val_mean_squared_error: 0.3907 - val_mean_absolute_error: 0.4760 Epoch 56/200 856/856 [==============================] - 0s 147us/sample - loss: 1.1858 - mean_squared_error: 1.1858 - mean_absolute_error: 0.8667 - val_loss: 0.3469 - val_mean_squared_error: 0.3469 - val_mean_absolute_error: 0.4459 Epoch 57/200 856/856 [==============================] - 0s 151us/sample - loss: 1.1542 - mean_squared_error: 1.1542 - mean_absolute_error: 0.8430 - val_loss: 0.3541 - val_mean_squared_error: 0.3541 - val_mean_absolute_error: 0.4492 Epoch 58/200 856/856 [==============================] - 0s 162us/sample - loss: 1.1515 - mean_squared_error: 1.1515 - mean_absolute_error: 0.8466 - val_loss: 0.3560 - val_mean_squared_error: 0.3560 - val_mean_absolute_error: 0.4522 Epoch 59/200 856/856 [==============================] - 0s 166us/sample - loss: 1.1508 - mean_squared_error: 1.1508 - mean_absolute_error: 0.8495 - val_loss: 0.4014 - val_mean_squared_error: 0.4014 - val_mean_absolute_error: 0.4813 Epoch 60/200 856/856 [==============================] - 0s 132us/sample - loss: 1.1728 - mean_squared_error: 1.1728 - mean_absolute_error: 0.8437 - val_loss: 0.3930 - val_mean_squared_error: 0.3930 - val_mean_absolute_error: 0.4731 Epoch 61/200 856/856 [==============================] - 0s 138us/sample - loss: 1.1526 - mean_squared_error: 1.1526 - mean_absolute_error: 0.8308 - val_loss: 0.3308 - val_mean_squared_error: 0.3308 - val_mean_absolute_error: 0.4403 Epoch 62/200 856/856 [==============================] - 0s 150us/sample - loss: 1.0861 - mean_squared_error: 1.0861 - mean_absolute_error: 0.8194 - val_loss: 0.3616 - val_mean_squared_error: 0.3616 - val_mean_absolute_error: 0.4590 Epoch 63/200 856/856 [==============================] - 0s 147us/sample - loss: 1.1102 - mean_squared_error: 1.1102 - mean_absolute_error: 0.8256 - val_loss: 0.3528 - val_mean_squared_error: 0.3528 - val_mean_absolute_error: 0.4587 Epoch 64/200 856/856 [==============================] - 0s 141us/sample - loss: 1.0456 - mean_squared_error: 1.0456 - mean_absolute_error: 0.8000 - val_loss: 0.3362 - val_mean_squared_error: 0.3362 - val_mean_absolute_error: 0.4472 Epoch 65/200 856/856 [==============================] - 0s 148us/sample - loss: 1.1004 - mean_squared_error: 1.1004 - mean_absolute_error: 0.8299 - val_loss: 0.3534 - val_mean_squared_error: 0.3534 - val_mean_absolute_error: 0.4521 Epoch 66/200 856/856 [==============================] - 0s 151us/sample - loss: 1.0562 - mean_squared_error: 1.0562 - mean_absolute_error: 0.7887 - val_loss: 0.3667 - val_mean_squared_error: 0.3667 - val_mean_absolute_error: 0.4633 Epoch 67/200 856/856 [==============================] - 0s 145us/sample - loss: 1.0206 - mean_squared_error: 1.0206 - mean_absolute_error: 0.7946 - val_loss: 0.3458 - val_mean_squared_error: 0.3458 - val_mean_absolute_error: 0.4516 Epoch 68/200 856/856 [==============================] - 0s 148us/sample - loss: 1.0377 - mean_squared_error: 1.0377 - mean_absolute_error: 0.8007 - val_loss: 0.3460 - val_mean_squared_error: 0.3460 - val_mean_absolute_error: 0.4463 Epoch 69/200 856/856 [==============================] - 0s 151us/sample - loss: 1.0853 - mean_squared_error: 1.0853 - mean_absolute_error: 0.8061 - val_loss: 0.3234 - val_mean_squared_error: 0.3234 - val_mean_absolute_error: 0.4358 Epoch 70/200 856/856 [==============================] - 0s 144us/sample - loss: 0.9998 - mean_squared_error: 0.9998 - mean_absolute_error: 0.7890 - val_loss: 0.3292 - val_mean_squared_error: 0.3292 - val_mean_absolute_error: 0.4418 Epoch 71/200 856/856 [==============================] - 0s 155us/sample - loss: 1.0049 - mean_squared_error: 1.0049 - mean_absolute_error: 0.7845 - val_loss: 0.3320 - val_mean_squared_error: 0.3320 - val_mean_absolute_error: 0.4424 Epoch 72/200 856/856 [==============================] - 0s 145us/sample - loss: 0.9582 - mean_squared_error: 0.9582 - mean_absolute_error: 0.7731 - val_loss: 0.3633 - val_mean_squared_error: 0.3633 - val_mean_absolute_error: 0.4601 Epoch 73/200 856/856 [==============================] - 0s 148us/sample - loss: 1.0058 - mean_squared_error: 1.0058 - mean_absolute_error: 0.7918 - val_loss: 0.3568 - val_mean_squared_error: 0.3568 - val_mean_absolute_error: 0.4551 Epoch 74/200 856/856 [==============================] - 0s 153us/sample - loss: 0.9598 - mean_squared_error: 0.9598 - mean_absolute_error: 0.7609 - val_loss: 0.3578 - val_mean_squared_error: 0.3578 - val_mean_absolute_error: 0.4598 Epoch 75/200 856/856 [==============================] - 0s 153us/sample - loss: 0.9917 - mean_squared_error: 0.9917 - mean_absolute_error: 0.7749 - val_loss: 0.3425 - val_mean_squared_error: 0.3425 - val_mean_absolute_error: 0.4550 Epoch 76/200 856/856 [==============================] - 0s 151us/sample - loss: 1.0454 - mean_squared_error: 1.0454 - mean_absolute_error: 0.8074 - val_loss: 0.3489 - val_mean_squared_error: 0.3489 - val_mean_absolute_error: 0.4545 Epoch 77/200 856/856 [==============================] - 0s 150us/sample - loss: 0.8903 - mean_squared_error: 0.8903 - mean_absolute_error: 0.7416 - val_loss: 0.3349 - val_mean_squared_error: 0.3349 - val_mean_absolute_error: 0.4416 Epoch 78/200 856/856 [==============================] - 0s 145us/sample - loss: 1.0474 - mean_squared_error: 1.0474 - mean_absolute_error: 0.8027 - val_loss: 0.3336 - val_mean_squared_error: 0.3336 - val_mean_absolute_error: 0.4393 Epoch 79/200 856/856 [==============================] - 0s 148us/sample - loss: 0.9318 - mean_squared_error: 0.9318 - mean_absolute_error: 0.7625 - val_loss: 0.3563 - val_mean_squared_error: 0.3563 - val_mean_absolute_error: 0.4522 Epoch 80/200 856/856 [==============================] - 0s 150us/sample - loss: 0.9156 - mean_squared_error: 0.9156 - mean_absolute_error: 0.7320 - val_loss: 0.3394 - val_mean_squared_error: 0.3394 - val_mean_absolute_error: 0.4470 Epoch 81/200 856/856 [==============================] - 0s 147us/sample - loss: 0.9023 - mean_squared_error: 0.9023 - mean_absolute_error: 0.7364 - val_loss: 0.3454 - val_mean_squared_error: 0.3454 - val_mean_absolute_error: 0.4509 Epoch 82/200 856/856 [==============================] - 0s 148us/sample - loss: 0.9380 - mean_squared_error: 0.9380 - mean_absolute_error: 0.7613 - val_loss: 0.3445 - val_mean_squared_error: 0.3445 - val_mean_absolute_error: 0.4529 Epoch 83/200 856/856 [==============================] - 0s 147us/sample - loss: 0.8765 - mean_squared_error: 0.8765 - mean_absolute_error: 0.7236 - val_loss: 0.3277 - val_mean_squared_error: 0.3277 - val_mean_absolute_error: 0.4413 Epoch 84/200 856/856 [==============================] - 0s 147us/sample - loss: 0.8396 - mean_squared_error: 0.8396 - mean_absolute_error: 0.7351 - val_loss: 0.3638 - val_mean_squared_error: 0.3638 - val_mean_absolute_error: 0.4636 Epoch 85/200 856/856 [==============================] - 0s 147us/sample - loss: 0.9005 - mean_squared_error: 0.9005 - mean_absolute_error: 0.7382 - val_loss: 0.3437 - val_mean_squared_error: 0.3437 - val_mean_absolute_error: 0.4445 Epoch 86/200 856/856 [==============================] - 0s 147us/sample - loss: 0.8756 - mean_squared_error: 0.8756 - mean_absolute_error: 0.7336 - val_loss: 0.3269 - val_mean_squared_error: 0.3269 - val_mean_absolute_error: 0.4351 Epoch 87/200 856/856 [==============================] - 0s 146us/sample - loss: 0.8850 - mean_squared_error: 0.8850 - mean_absolute_error: 0.7356 - val_loss: 0.3281 - val_mean_squared_error: 0.3281 - val_mean_absolute_error: 0.4428 Epoch 88/200 856/856 [==============================] - 0s 147us/sample - loss: 0.8428 - mean_squared_error: 0.8428 - mean_absolute_error: 0.7247 - val_loss: 0.3386 - val_mean_squared_error: 0.3386 - val_mean_absolute_error: 0.4488 Epoch 89/200 856/856 [==============================] - 0s 148us/sample - loss: 0.8499 - mean_squared_error: 0.8499 - mean_absolute_error: 0.7139 - val_loss: 0.3544 - val_mean_squared_error: 0.3544 - val_mean_absolute_error: 0.4540 Epoch 90/200 856/856 [==============================] - 0s 147us/sample - loss: 0.8551 - mean_squared_error: 0.8551 - mean_absolute_error: 0.7257 - val_loss: 0.3364 - val_mean_squared_error: 0.3364 - val_mean_absolute_error: 0.4395 Epoch 91/200 856/856 [==============================] - 0s 147us/sample - loss: 0.8303 - mean_squared_error: 0.8303 - mean_absolute_error: 0.7135 - val_loss: 0.3316 - val_mean_squared_error: 0.3316 - val_mean_absolute_error: 0.4395 Epoch 92/200 856/856 [==============================] - 0s 141us/sample - loss: 0.8816 - mean_squared_error: 0.8816 - mean_absolute_error: 0.7322 - val_loss: 0.3319 - val_mean_squared_error: 0.3319 - val_mean_absolute_error: 0.4416 Epoch 93/200 856/856 [==============================] - 0s 147us/sample - loss: 0.7980 - mean_squared_error: 0.7980 - mean_absolute_error: 0.7005 - val_loss: 0.3538 - val_mean_squared_error: 0.3538 - val_mean_absolute_error: 0.4545 Epoch 94/200 856/856 [==============================] - 0s 148us/sample - loss: 0.8809 - mean_squared_error: 0.8809 - mean_absolute_error: 0.7244 - val_loss: 0.3503 - val_mean_squared_error: 0.3503 - val_mean_absolute_error: 0.4528 Epoch 95/200 856/856 [==============================] - 0s 140us/sample - loss: 0.7647 - mean_squared_error: 0.7647 - mean_absolute_error: 0.6771 - val_loss: 0.3430 - val_mean_squared_error: 0.3430 - val_mean_absolute_error: 0.4490 Epoch 96/200 856/856 [==============================] - 0s 147us/sample - loss: 0.8204 - mean_squared_error: 0.8204 - mean_absolute_error: 0.7058 - val_loss: 0.3502 - val_mean_squared_error: 0.3502 - val_mean_absolute_error: 0.4555 Epoch 97/200 856/856 [==============================] - 0s 152us/sample - loss: 0.8228 - mean_squared_error: 0.8228 - mean_absolute_error: 0.7079 - val_loss: 0.3420 - val_mean_squared_error: 0.3420 - val_mean_absolute_error: 0.4528 Epoch 98/200 856/856 [==============================] - 0s 171us/sample - loss: 0.7235 - mean_squared_error: 0.7235 - mean_absolute_error: 0.6641 - val_loss: 0.3368 - val_mean_squared_error: 0.3368 - val_mean_absolute_error: 0.4521 Epoch 99/200 856/856 [==============================] - 0s 147us/sample - loss: 0.7540 - mean_squared_error: 0.7540 - mean_absolute_error: 0.6772 - val_loss: 0.3203 - val_mean_squared_error: 0.3203 - val_mean_absolute_error: 0.4335 Epoch 100/200 856/856 [==============================] - 0s 136us/sample - loss: 0.7806 - mean_squared_error: 0.7806 - mean_absolute_error: 0.6954 - val_loss: 0.3295 - val_mean_squared_error: 0.3295 - val_mean_absolute_error: 0.4444 Epoch 101/200 856/856 [==============================] - 0s 141us/sample - loss: 0.7549 - mean_squared_error: 0.7549 - mean_absolute_error: 0.6761 - val_loss: 0.3408 - val_mean_squared_error: 0.3408 - val_mean_absolute_error: 0.4498 Epoch 102/200 856/856 [==============================] - 0s 146us/sample - loss: 0.8110 - mean_squared_error: 0.8110 - mean_absolute_error: 0.6964 - val_loss: 0.3438 - val_mean_squared_error: 0.3438 - val_mean_absolute_error: 0.4509 Epoch 103/200 856/856 [==============================] - 0s 145us/sample - loss: 0.6770 - mean_squared_error: 0.6770 - mean_absolute_error: 0.6477 - val_loss: 0.3366 - val_mean_squared_error: 0.3366 - val_mean_absolute_error: 0.4512 Epoch 104/200 856/856 [==============================] - 0s 148us/sample - loss: 0.7376 - mean_squared_error: 0.7376 - mean_absolute_error: 0.6692 - val_loss: 0.3405 - val_mean_squared_error: 0.3405 - val_mean_absolute_error: 0.4473 Epoch 105/200 856/856 [==============================] - 0s 154us/sample - loss: 0.7675 - mean_squared_error: 0.7675 - mean_absolute_error: 0.6791 - val_loss: 0.3280 - val_mean_squared_error: 0.3280 - val_mean_absolute_error: 0.4446 Epoch 106/200 856/856 [==============================] - 0s 150us/sample - loss: 0.7151 - mean_squared_error: 0.7151 - mean_absolute_error: 0.6583 - val_loss: 0.3569 - val_mean_squared_error: 0.3569 - val_mean_absolute_error: 0.4670 Epoch 107/200 856/856 [==============================] - 0s 151us/sample - loss: 0.7248 - mean_squared_error: 0.7248 - mean_absolute_error: 0.6721 - val_loss: 0.3320 - val_mean_squared_error: 0.3320 - val_mean_absolute_error: 0.4479 Epoch 108/200 856/856 [==============================] - 0s 147us/sample - loss: 0.7621 - mean_squared_error: 0.7621 - mean_absolute_error: 0.6828 - val_loss: 0.3273 - val_mean_squared_error: 0.3273 - val_mean_absolute_error: 0.4449 Epoch 109/200 856/856 [==============================] - 0s 150us/sample - loss: 0.7629 - mean_squared_error: 0.7629 - mean_absolute_error: 0.6749 - val_loss: 0.3253 - val_mean_squared_error: 0.3253 - val_mean_absolute_error: 0.4452 Epoch 110/200 856/856 [==============================] - 0s 152us/sample - loss: 0.7198 - mean_squared_error: 0.7198 - mean_absolute_error: 0.6570 - val_loss: 0.3193 - val_mean_squared_error: 0.3193 - val_mean_absolute_error: 0.4373 Epoch 111/200 856/856 [==============================] - 0s 154us/sample - loss: 0.6877 - mean_squared_error: 0.6877 - mean_absolute_error: 0.6425 - val_loss: 0.3148 - val_mean_squared_error: 0.3148 - val_mean_absolute_error: 0.4314 Epoch 112/200 856/856 [==============================] - 0s 147us/sample - loss: 0.6775 - mean_squared_error: 0.6775 - mean_absolute_error: 0.6426 - val_loss: 0.3536 - val_mean_squared_error: 0.3536 - val_mean_absolute_error: 0.4584 Epoch 113/200 856/856 [==============================] - 0s 150us/sample - loss: 0.7140 - mean_squared_error: 0.7140 - mean_absolute_error: 0.6614 - val_loss: 0.3358 - val_mean_squared_error: 0.3358 - val_mean_absolute_error: 0.4465 Epoch 114/200 856/856 [==============================] - 0s 151us/sample - loss: 0.6659 - mean_squared_error: 0.6659 - mean_absolute_error: 0.6387 - val_loss: 0.3237 - val_mean_squared_error: 0.3237 - val_mean_absolute_error: 0.4410 Epoch 115/200 856/856 [==============================] - 0s 143us/sample - loss: 0.6399 - mean_squared_error: 0.6399 - mean_absolute_error: 0.6194 - val_loss: 0.3329 - val_mean_squared_error: 0.3329 - val_mean_absolute_error: 0.4462 Epoch 116/200 856/856 [==============================] - 0s 147us/sample - loss: 0.6403 - mean_squared_error: 0.6403 - mean_absolute_error: 0.6342 - val_loss: 0.3203 - val_mean_squared_error: 0.3203 - val_mean_absolute_error: 0.4395 Epoch 117/200 856/856 [==============================] - 0s 147us/sample - loss: 0.6597 - mean_squared_error: 0.6597 - mean_absolute_error: 0.6380 - val_loss: 0.3219 - val_mean_squared_error: 0.3219 - val_mean_absolute_error: 0.4396 Epoch 118/200 856/856 [==============================] - 0s 141us/sample - loss: 0.6568 - mean_squared_error: 0.6568 - mean_absolute_error: 0.6446 - val_loss: 0.3294 - val_mean_squared_error: 0.3294 - val_mean_absolute_error: 0.4427 Epoch 119/200 856/856 [==============================] - 0s 147us/sample - loss: 0.6384 - mean_squared_error: 0.6384 - mean_absolute_error: 0.6169 - val_loss: 0.3200 - val_mean_squared_error: 0.3200 - val_mean_absolute_error: 0.4380 Epoch 120/200 856/856 [==============================] - 0s 153us/sample - loss: 0.6309 - mean_squared_error: 0.6309 - mean_absolute_error: 0.6151 - val_loss: 0.3304 - val_mean_squared_error: 0.3304 - val_mean_absolute_error: 0.4451 Epoch 121/200 856/856 [==============================] - 0s 154us/sample - loss: 0.6597 - mean_squared_error: 0.6597 - mean_absolute_error: 0.6318 - val_loss: 0.3232 - val_mean_squared_error: 0.3232 - val_mean_absolute_error: 0.4385 Epoch 122/200 856/856 [==============================] - 0s 145us/sample - loss: 0.6317 - mean_squared_error: 0.6317 - mean_absolute_error: 0.6208 - val_loss: 0.3242 - val_mean_squared_error: 0.3242 - val_mean_absolute_error: 0.4373 Epoch 123/200 856/856 [==============================] - 0s 138us/sample - loss: 0.6360 - mean_squared_error: 0.6360 - mean_absolute_error: 0.6203 - val_loss: 0.3250 - val_mean_squared_error: 0.3250 - val_mean_absolute_error: 0.4441 Epoch 124/200 856/856 [==============================] - 0s 154us/sample - loss: 0.6326 - mean_squared_error: 0.6326 - mean_absolute_error: 0.6236 - val_loss: 0.3130 - val_mean_squared_error: 0.3130 - val_mean_absolute_error: 0.4353 Epoch 125/200 856/856 [==============================] - 0s 138us/sample - loss: 0.6136 - mean_squared_error: 0.6136 - mean_absolute_error: 0.6066 - val_loss: 0.3229 - val_mean_squared_error: 0.3229 - val_mean_absolute_error: 0.4435 Epoch 126/200 856/856 [==============================] - 0s 140us/sample - loss: 0.6667 - mean_squared_error: 0.6667 - mean_absolute_error: 0.6395 - val_loss: 0.3245 - val_mean_squared_error: 0.3245 - val_mean_absolute_error: 0.4450 Epoch 127/200 856/856 [==============================] - 0s 139us/sample - loss: 0.5841 - mean_squared_error: 0.5841 - mean_absolute_error: 0.6057 - val_loss: 0.3191 - val_mean_squared_error: 0.3191 - val_mean_absolute_error: 0.4400 Epoch 128/200 856/856 [==============================] - 0s 146us/sample - loss: 0.6070 - mean_squared_error: 0.6070 - mean_absolute_error: 0.6041 - val_loss: 0.3261 - val_mean_squared_error: 0.3261 - val_mean_absolute_error: 0.4458 Epoch 129/200 856/856 [==============================] - 0s 140us/sample - loss: 0.5996 - mean_squared_error: 0.5996 - mean_absolute_error: 0.5942 - val_loss: 0.3231 - val_mean_squared_error: 0.3231 - val_mean_absolute_error: 0.4389 Epoch 130/200 856/856 [==============================] - 0s 140us/sample - loss: 0.6061 - mean_squared_error: 0.6061 - mean_absolute_error: 0.6027 - val_loss: 0.3209 - val_mean_squared_error: 0.3209 - val_mean_absolute_error: 0.4393 Epoch 131/200 856/856 [==============================] - 0s 146us/sample - loss: 0.5877 - mean_squared_error: 0.5877 - mean_absolute_error: 0.5821 - val_loss: 0.3242 - val_mean_squared_error: 0.3242 - val_mean_absolute_error: 0.4436 Epoch 132/200 856/856 [==============================] - 0s 141us/sample - loss: 0.5463 - mean_squared_error: 0.5463 - mean_absolute_error: 0.5751 - val_loss: 0.3211 - val_mean_squared_error: 0.3211 - val_mean_absolute_error: 0.4404 Epoch 133/200 856/856 [==============================] - 0s 150us/sample - loss: 0.5881 - mean_squared_error: 0.5881 - mean_absolute_error: 0.6052 - val_loss: 0.3242 - val_mean_squared_error: 0.3242 - val_mean_absolute_error: 0.4443 Epoch 134/200 856/856 [==============================] - 0s 152us/sample - loss: 0.6268 - mean_squared_error: 0.6268 - mean_absolute_error: 0.6166 - val_loss: 0.3271 - val_mean_squared_error: 0.3271 - val_mean_absolute_error: 0.4474 Epoch 135/200 856/856 [==============================] - 0s 146us/sample - loss: 0.6066 - mean_squared_error: 0.6066 - mean_absolute_error: 0.6071 - val_loss: 0.3262 - val_mean_squared_error: 0.3262 - val_mean_absolute_error: 0.4478 Epoch 136/200 856/856 [==============================] - 0s 153us/sample - loss: 0.5979 - mean_squared_error: 0.5979 - mean_absolute_error: 0.5967 - val_loss: 0.3221 - val_mean_squared_error: 0.3221 - val_mean_absolute_error: 0.4422 Epoch 137/200 856/856 [==============================] - 0s 147us/sample - loss: 0.6071 - mean_squared_error: 0.6071 - mean_absolute_error: 0.6031 - val_loss: 0.3174 - val_mean_squared_error: 0.3174 - val_mean_absolute_error: 0.4375 Epoch 138/200 856/856 [==============================] - 0s 186us/sample - loss: 0.5618 - mean_squared_error: 0.5618 - mean_absolute_error: 0.5875 - val_loss: 0.3279 - val_mean_squared_error: 0.3279 - val_mean_absolute_error: 0.4482 Epoch 139/200 856/856 [==============================] - 0s 155us/sample - loss: 0.5762 - mean_squared_error: 0.5762 - mean_absolute_error: 0.5863 - val_loss: 0.3226 - val_mean_squared_error: 0.3226 - val_mean_absolute_error: 0.4454 Epoch 140/200 856/856 [==============================] - 0s 158us/sample - loss: 0.5916 - mean_squared_error: 0.5916 - mean_absolute_error: 0.5942 - val_loss: 0.3268 - val_mean_squared_error: 0.3268 - val_mean_absolute_error: 0.4466 Epoch 141/200 856/856 [==============================] - 0s 164us/sample - loss: 0.5680 - mean_squared_error: 0.5680 - mean_absolute_error: 0.5822 - val_loss: 0.3219 - val_mean_squared_error: 0.3219 - val_mean_absolute_error: 0.4434 Epoch 142/200 856/856 [==============================] - 0s 157us/sample - loss: 0.5853 - mean_squared_error: 0.5853 - mean_absolute_error: 0.5854 - val_loss: 0.3294 - val_mean_squared_error: 0.3294 - val_mean_absolute_error: 0.4479 Epoch 143/200 856/856 [==============================] - 0s 165us/sample - loss: 0.5306 - mean_squared_error: 0.5306 - mean_absolute_error: 0.5737 - val_loss: 0.3271 - val_mean_squared_error: 0.3271 - val_mean_absolute_error: 0.4475 Epoch 144/200 856/856 [==============================] - 0s 133us/sample - loss: 0.5473 - mean_squared_error: 0.5473 - mean_absolute_error: 0.5699 - val_loss: 0.3250 - val_mean_squared_error: 0.3250 - val_mean_absolute_error: 0.4457 Epoch 145/200 856/856 [==============================] - 0s 139us/sample - loss: 0.5292 - mean_squared_error: 0.5292 - mean_absolute_error: 0.5614 - val_loss: 0.3316 - val_mean_squared_error: 0.3316 - val_mean_absolute_error: 0.4503 Epoch 146/200 856/856 [==============================] - 0s 143us/sample - loss: 0.5552 - mean_squared_error: 0.5552 - mean_absolute_error: 0.5764 - val_loss: 0.3223 - val_mean_squared_error: 0.3223 - val_mean_absolute_error: 0.4435 Epoch 147/200 856/856 [==============================] - 0s 140us/sample - loss: 0.5457 - mean_squared_error: 0.5457 - mean_absolute_error: 0.5740 - val_loss: 0.3263 - val_mean_squared_error: 0.3263 - val_mean_absolute_error: 0.4477 Epoch 148/200 856/856 [==============================] - 0s 146us/sample - loss: 0.5829 - mean_squared_error: 0.5829 - mean_absolute_error: 0.5819 - val_loss: 0.3327 - val_mean_squared_error: 0.3327 - val_mean_absolute_error: 0.4553 Epoch 149/200 856/856 [==============================] - 0s 144us/sample - loss: 0.5479 - mean_squared_error: 0.5479 - mean_absolute_error: 0.5739 - val_loss: 0.3296 - val_mean_squared_error: 0.3296 - val_mean_absolute_error: 0.4531 Epoch 150/200 856/856 [==============================] - 0s 143us/sample - loss: 0.5537 - mean_squared_error: 0.5537 - mean_absolute_error: 0.5743 - val_loss: 0.3269 - val_mean_squared_error: 0.3269 - val_mean_absolute_error: 0.4518 Epoch 151/200 856/856 [==============================] - 0s 150us/sample - loss: 0.5512 - mean_squared_error: 0.5512 - mean_absolute_error: 0.5748 - val_loss: 0.3341 - val_mean_squared_error: 0.3341 - val_mean_absolute_error: 0.4557 Epoch 152/200 856/856 [==============================] - 0s 139us/sample - loss: 0.5338 - mean_squared_error: 0.5338 - mean_absolute_error: 0.5586 - val_loss: 0.3259 - val_mean_squared_error: 0.3259 - val_mean_absolute_error: 0.4489 Epoch 153/200 856/856 [==============================] - 0s 147us/sample - loss: 0.5383 - mean_squared_error: 0.5383 - mean_absolute_error: 0.5605 - val_loss: 0.3311 - val_mean_squared_error: 0.3311 - val_mean_absolute_error: 0.4509 Epoch 154/200 856/856 [==============================] - 0s 146us/sample - loss: 0.5283 - mean_squared_error: 0.5283 - mean_absolute_error: 0.5565 - val_loss: 0.3250 - val_mean_squared_error: 0.3250 - val_mean_absolute_error: 0.4469 Epoch 155/200 856/856 [==============================] - 0s 139us/sample - loss: 0.5281 - mean_squared_error: 0.5281 - mean_absolute_error: 0.5580 - val_loss: 0.3279 - val_mean_squared_error: 0.3279 - val_mean_absolute_error: 0.4504 Epoch 156/200 856/856 [==============================] - 0s 148us/sample - loss: 0.5377 - mean_squared_error: 0.5377 - mean_absolute_error: 0.5606 - val_loss: 0.3364 - val_mean_squared_error: 0.3364 - val_mean_absolute_error: 0.4554 Epoch 157/200 856/856 [==============================] - 0s 150us/sample - loss: 0.5383 - mean_squared_error: 0.5383 - mean_absolute_error: 0.5667 - val_loss: 0.3285 - val_mean_squared_error: 0.3285 - val_mean_absolute_error: 0.4505 Epoch 158/200 856/856 [==============================] - 0s 140us/sample - loss: 0.5124 - mean_squared_error: 0.5124 - mean_absolute_error: 0.5587 - val_loss: 0.3320 - val_mean_squared_error: 0.3320 - val_mean_absolute_error: 0.4530 Epoch 159/200 856/856 [==============================] - 0s 152us/sample - loss: 0.5204 - mean_squared_error: 0.5204 - mean_absolute_error: 0.5527 - val_loss: 0.3278 - val_mean_squared_error: 0.3278 - val_mean_absolute_error: 0.4491 Epoch 160/200 856/856 [==============================] - 0s 147us/sample - loss: 0.5298 - mean_squared_error: 0.5298 - mean_absolute_error: 0.5601 - val_loss: 0.3388 - val_mean_squared_error: 0.3388 - val_mean_absolute_error: 0.4555 Epoch 161/200 856/856 [==============================] - 0s 146us/sample - loss: 0.5316 - mean_squared_error: 0.5316 - mean_absolute_error: 0.5638 - val_loss: 0.3312 - val_mean_squared_error: 0.3312 - val_mean_absolute_error: 0.4535 Epoch 162/200 856/856 [==============================] - 0s 148us/sample - loss: 0.5027 - mean_squared_error: 0.5027 - mean_absolute_error: 0.5552 - val_loss: 0.3309 - val_mean_squared_error: 0.3309 - val_mean_absolute_error: 0.4560 Epoch 163/200 856/856 [==============================] - 0s 146us/sample - loss: 0.5146 - mean_squared_error: 0.5146 - mean_absolute_error: 0.5552 - val_loss: 0.3356 - val_mean_squared_error: 0.3356 - val_mean_absolute_error: 0.4566 Epoch 164/200 856/856 [==============================] - 0s 148us/sample - loss: 0.4861 - mean_squared_error: 0.4861 - mean_absolute_error: 0.5410 - val_loss: 0.3261 - val_mean_squared_error: 0.3261 - val_mean_absolute_error: 0.4489 Epoch 165/200 856/856 [==============================] - 0s 148us/sample - loss: 0.5042 - mean_squared_error: 0.5042 - mean_absolute_error: 0.5545 - val_loss: 0.3229 - val_mean_squared_error: 0.3229 - val_mean_absolute_error: 0.4456 Epoch 166/200 856/856 [==============================] - 0s 144us/sample - loss: 0.4974 - mean_squared_error: 0.4974 - mean_absolute_error: 0.5408 - val_loss: 0.3250 - val_mean_squared_error: 0.3250 - val_mean_absolute_error: 0.4456 Epoch 167/200 856/856 [==============================] - 0s 150us/sample - loss: 0.5154 - mean_squared_error: 0.5154 - mean_absolute_error: 0.5561 - val_loss: 0.3294 - val_mean_squared_error: 0.3294 - val_mean_absolute_error: 0.4509 Epoch 168/200 856/856 [==============================] - 0s 141us/sample - loss: 0.5094 - mean_squared_error: 0.5094 - mean_absolute_error: 0.5485 - val_loss: 0.3292 - val_mean_squared_error: 0.3292 - val_mean_absolute_error: 0.4492 Epoch 169/200 856/856 [==============================] - 0s 148us/sample - loss: 0.5087 - mean_squared_error: 0.5087 - mean_absolute_error: 0.5525 - val_loss: 0.3208 - val_mean_squared_error: 0.3208 - val_mean_absolute_error: 0.4444 Epoch 170/200 856/856 [==============================] - 0s 148us/sample - loss: 0.4773 - mean_squared_error: 0.4773 - mean_absolute_error: 0.5372 - val_loss: 0.3235 - val_mean_squared_error: 0.3235 - val_mean_absolute_error: 0.4466 Epoch 171/200 856/856 [==============================] - 0s 139us/sample - loss: 0.5081 - mean_squared_error: 0.5081 - mean_absolute_error: 0.5523 - val_loss: 0.3237 - val_mean_squared_error: 0.3237 - val_mean_absolute_error: 0.4473 Epoch 172/200 856/856 [==============================] - 0s 144us/sample - loss: 0.4962 - mean_squared_error: 0.4962 - mean_absolute_error: 0.5371 - val_loss: 0.3306 - val_mean_squared_error: 0.3306 - val_mean_absolute_error: 0.4527 Epoch 173/200 856/856 [==============================] - 0s 134us/sample - loss: 0.4816 - mean_squared_error: 0.4816 - mean_absolute_error: 0.5326 - val_loss: 0.3209 - val_mean_squared_error: 0.3209 - val_mean_absolute_error: 0.4463 Epoch 174/200 856/856 [==============================] - 0s 141us/sample - loss: 0.4807 - mean_squared_error: 0.4807 - mean_absolute_error: 0.5304 - val_loss: 0.3274 - val_mean_squared_error: 0.3274 - val_mean_absolute_error: 0.4519 Epoch 175/200 856/856 [==============================] - 0s 153us/sample - loss: 0.4835 - mean_squared_error: 0.4835 - mean_absolute_error: 0.5291 - val_loss: 0.3232 - val_mean_squared_error: 0.3232 - val_mean_absolute_error: 0.4480 Epoch 176/200 856/856 [==============================] - 0s 147us/sample - loss: 0.5052 - mean_squared_error: 0.5052 - mean_absolute_error: 0.5466 - val_loss: 0.3252 - val_mean_squared_error: 0.3252 - val_mean_absolute_error: 0.4509 Epoch 177/200 856/856 [==============================] - 0s 161us/sample - loss: 0.4732 - mean_squared_error: 0.4732 - mean_absolute_error: 0.5323 - val_loss: 0.3288 - val_mean_squared_error: 0.3288 - val_mean_absolute_error: 0.4488 Epoch 178/200 856/856 [==============================] - 0s 165us/sample - loss: 0.4931 - mean_squared_error: 0.4931 - mean_absolute_error: 0.5426 - val_loss: 0.3282 - val_mean_squared_error: 0.3282 - val_mean_absolute_error: 0.4478 Epoch 179/200 856/856 [==============================] - 0s 138us/sample - loss: 0.5424 - mean_squared_error: 0.5424 - mean_absolute_error: 0.5687 - val_loss: 0.3340 - val_mean_squared_error: 0.3340 - val_mean_absolute_error: 0.4526 Epoch 180/200 856/856 [==============================] - 0s 147us/sample - loss: 0.4497 - mean_squared_error: 0.4497 - mean_absolute_error: 0.5239 - val_loss: 0.3255 - val_mean_squared_error: 0.3255 - val_mean_absolute_error: 0.4479 Epoch 181/200 856/856 [==============================] - 0s 152us/sample - loss: 0.5013 - mean_squared_error: 0.5013 - mean_absolute_error: 0.5392 - val_loss: 0.3269 - val_mean_squared_error: 0.3269 - val_mean_absolute_error: 0.4474 Epoch 182/200 856/856 [==============================] - 0s 153us/sample - loss: 0.4942 - mean_squared_error: 0.4942 - mean_absolute_error: 0.5399 - val_loss: 0.3253 - val_mean_squared_error: 0.3253 - val_mean_absolute_error: 0.4442 Epoch 183/200 856/856 [==============================] - 0s 140us/sample - loss: 0.4810 - mean_squared_error: 0.4810 - mean_absolute_error: 0.5363 - val_loss: 0.3265 - val_mean_squared_error: 0.3265 - val_mean_absolute_error: 0.4427 Epoch 184/200 856/856 [==============================] - 0s 139us/sample - loss: 0.4749 - mean_squared_error: 0.4749 - mean_absolute_error: 0.5322 - val_loss: 0.3238 - val_mean_squared_error: 0.3238 - val_mean_absolute_error: 0.4431 Epoch 185/200 856/856 [==============================] - 0s 138us/sample - loss: 0.4823 - mean_squared_error: 0.4823 - mean_absolute_error: 0.5315 - val_loss: 0.3283 - val_mean_squared_error: 0.3283 - val_mean_absolute_error: 0.4480 Epoch 186/200 856/856 [==============================] - 0s 139us/sample - loss: 0.4795 - mean_squared_error: 0.4795 - mean_absolute_error: 0.5388 - val_loss: 0.3346 - val_mean_squared_error: 0.3346 - val_mean_absolute_error: 0.4528 Epoch 187/200 856/856 [==============================] - 0s 147us/sample - loss: 0.4788 - mean_squared_error: 0.4788 - mean_absolute_error: 0.5358 - val_loss: 0.3298 - val_mean_squared_error: 0.3298 - val_mean_absolute_error: 0.4484 Epoch 188/200 856/856 [==============================] - 0s 140us/sample - loss: 0.4865 - mean_squared_error: 0.4865 - mean_absolute_error: 0.5375 - val_loss: 0.3324 - val_mean_squared_error: 0.3324 - val_mean_absolute_error: 0.4488 Epoch 189/200 856/856 [==============================] - 0s 140us/sample - loss: 0.4803 - mean_squared_error: 0.4803 - mean_absolute_error: 0.5343 - val_loss: 0.3225 - val_mean_squared_error: 0.3225 - val_mean_absolute_error: 0.4418 Epoch 190/200 856/856 [==============================] - 0s 150us/sample - loss: 0.4720 - mean_squared_error: 0.4720 - mean_absolute_error: 0.5299 - val_loss: 0.3241 - val_mean_squared_error: 0.3241 - val_mean_absolute_error: 0.4448 Epoch 191/200 856/856 [==============================] - 0s 148us/sample - loss: 0.4611 - mean_squared_error: 0.4611 - mean_absolute_error: 0.5289 - val_loss: 0.3253 - val_mean_squared_error: 0.3253 - val_mean_absolute_error: 0.4455 Epoch 192/200 856/856 [==============================] - 0s 147us/sample - loss: 0.4795 - mean_squared_error: 0.4795 - mean_absolute_error: 0.5296 - val_loss: 0.3297 - val_mean_squared_error: 0.3297 - val_mean_absolute_error: 0.4478 Epoch 193/200 856/856 [==============================] - 0s 137us/sample - loss: 0.4742 - mean_squared_error: 0.4742 - mean_absolute_error: 0.5342 - val_loss: 0.3204 - val_mean_squared_error: 0.3204 - val_mean_absolute_error: 0.4410 Epoch 194/200 856/856 [==============================] - 0s 146us/sample - loss: 0.4808 - mean_squared_error: 0.4808 - mean_absolute_error: 0.5291 - val_loss: 0.3218 - val_mean_squared_error: 0.3218 - val_mean_absolute_error: 0.4442 Epoch 195/200 856/856 [==============================] - 0s 153us/sample - loss: 0.4609 - mean_squared_error: 0.4609 - mean_absolute_error: 0.5247 - val_loss: 0.3183 - val_mean_squared_error: 0.3183 - val_mean_absolute_error: 0.4367 Epoch 196/200 856/856 [==============================] - 0s 150us/sample - loss: 0.4820 - mean_squared_error: 0.4820 - mean_absolute_error: 0.5351 - val_loss: 0.3202 - val_mean_squared_error: 0.3202 - val_mean_absolute_error: 0.4379 Epoch 197/200 856/856 [==============================] - 0s 146us/sample - loss: 0.4690 - mean_squared_error: 0.4690 - mean_absolute_error: 0.5253 - val_loss: 0.3217 - val_mean_squared_error: 0.3217 - val_mean_absolute_error: 0.4405 Epoch 198/200 856/856 [==============================] - 0s 152us/sample - loss: 0.4554 - mean_squared_error: 0.4554 - mean_absolute_error: 0.5197 - val_loss: 0.3288 - val_mean_squared_error: 0.3288 - val_mean_absolute_error: 0.4451 Epoch 199/200 856/856 [==============================] - 0s 146us/sample - loss: 0.4575 - mean_squared_error: 0.4575 - mean_absolute_error: 0.5196 - val_loss: 0.3200 - val_mean_squared_error: 0.3200 - val_mean_absolute_error: 0.4375 Epoch 200/200 856/856 [==============================] - 0s 148us/sample - loss: 0.4698 - mean_squared_error: 0.4698 - mean_absolute_error: 0.5225 - val_loss: 0.3203 - val_mean_squared_error: 0.3203 - val_mean_absolute_error: 0.4400
print(history.history.keys())
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
dict_keys(['loss', 'mean_squared_error', 'mean_absolute_error', 'val_loss', 'val_mean_squared_error', 'val_mean_absolute_error'])
y_pred = model1.predict(X_test)
mean_squared_error(y_test, y_pred)
0.4065575532023555
Observations:The model above has shown some improvement in the accuracy when compared with the pca model above
model.save("ModelTrainedOnPC.h5")
model1.save("Model.h5")